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Article

Empirical Study of the Impact of Outward Foreign Direct Investment on Water Footprint Benefit in China

by
Daxue KAN
1,* and
Weichiao HUANG
2
1
School of Economics and Trade, Nanchang Institute of Technology, No. 289 Tianxiang Road, Nanchang 330099, China
2
Department of Economics, Western Michigan University, 1903 W. Michigan Ave., Kalamazoo, MI 49008, USA
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(16), 4409; https://doi.org/10.3390/su11164409
Submission received: 19 July 2019 / Revised: 6 August 2019 / Accepted: 13 August 2019 / Published: 15 August 2019
(This article belongs to the Section Environmental Sustainability and Applications)
Review Reports Versions Notes

Abstract

:
How to enhance the water footprint benefit in conjunction with outward foreign direct investment (OFDI) is of great significance to reconcile the contradiction between supply and demand of water resources. This paper examines the effect of OFDI on the water footprint benefit using system GMM (Generalized Method of Moments) on a dynamic panel data. The results revealed that, in general, OFDI was not conducive to enhancing social, spatial, and environmental benefits of China’s water footprint, but was conducive for improving water footprint economic benefits. The results also showed that different types of OFDI exert differential effects on water footprint benefits. Specifically, the market-seeking and resource-seeking types of OFDI are not conducive for enhancing social and spatial benefits of China’s water footprint, but have improved (although not significantly) economic benefits of the water footprint. However, the market-seeking type of OFDI is conducive for improving environmental benefits of the water footprint, while the resource-seeking OFDI is not conducive for improving environmental benefits of the water footprint. In addition, the technology-seeking OFDI is conducive to the social, economic, spatial, and environmental benefits of China’s water footprint. Furthermore, the path-wise OFDI (investing in developing countries) is not conducive to enhancing social, spatial, and environmental benefits of China’s water footprint, but has improved (although not significantly) the economic benefits of China’s water footprint. On the other hand, the inverse OFDI (investing in developed countries) is conducive to China’s water footprint including its social, economic, spatial, and environmental benefits. The findings from this study have relevant policy implications and can help provide some policy prescriptions for an economy such as China to engage in OFDI and enhance water footprint benefits. For instance, in addition to expanding market-seeking and resource- seeking OFDI, China should actively increase the scale of technology-seeking OFDI. In addition, while continuing to expand path-wise OFDI, China should further increase the scale of inverse OFDI. By taking advantage of the complementary and synergetic effects of different types of OFDI, an economy can capture the whole effects of OFDI to reap the water footprint’s full social, economic, spatial, and environmental benefits.

1. Introduction

China has been actively implementing the “going out” strategy and has achieved remarkable results in outward foreign direct investment (OFDI). Its OFDI has increased enormously from $0.044 billion USD at the beginning of “reform and opening up” to $158.29 billion USD in 2017, which amounts to an average annual growth rate of 25.54%. China’s flow of OFDI is now ranked third in the world, closely behind the U.S. and Japan. Additionally, its stock of OFDI had reached $1809.04 billion by the end of 2017, which rises to the second in the world. By 2017, China’s OFDI flow had been greater than that of its FDI inflows for three consecutive years. During the same period, China’s total water consumption has increased substantially, reaching 609 billion m3 in 2017, which is an increase of 155.3 billion m3 compared with 443.7 billion m3 at the beginning of the “reform and opening up.” China’s water consumption per capita is currently 439 m3. Presently, China’s exploitable water resources account for less than 40% of the total water resources, and the per capita water resources are only 1/4 of the world’s per capita water resources. Nearly 80% of China’s provinces and regions are in a state of water shortage. In addition, China has a serious water pollution problem, which makes the contradiction between supply and demand of water resources more acute in the process of economic development. Water shortage has become one of the factors inhibiting high-quality economic growth. In 2016, the proportion of water resources that failed to meet the drinking water source quality standards in rivers, lakes, and provincial boundaries reached 28.8%, 33.9%, and 32.9%, respectively. Additionally, 60.1% of the 6124 groundwater quality monitoring sites registered poor and very poor water quality counts. The five major river basins (Haihe River, Liaohe River, Yellow River, Huaihe River, and Songhua River) all exceed the standard of tolerable poor water quality. Worse yet, acid rain caused further damage to water quality, with 7.2% of the total territory severely inflicted by acid rain. In China, the main source of urban water supply comes from surface water or groundwater, or a combination of the two. Water pollution leads to deterioration of the water quality and deterioration of the water ecological environment, which is harmful for the construction of ecological civilization and sustainable utilization of water resources in China. Hence, it is essential to improve the water footprint benefit (water footprint benefit refers to the water footprint consumed by unit population, unit economic output, and unit space in a region, and the proportion of wastewater discharged from the water footprint) to alleviate the contradiction between supply and demand of water resources, and to reduce the total amount of water used and water pollution. However, most researchers have focused on studying the influencing factors of the water footprint. Furthermore, since the quality of China’s OFDI has been on the low side, it is worth studying which type of OFDI should be added to enhance the quality of OFDI. Yet there has been no work in the literature that considers the quality of OFDI from the perspective of the water footprint benefit. Additionally, fewer attention has been paid to studying the impact of China’s OFDI on the water footprint benefit. It is quite conceivable that OFDI can affect the water footprint benefit by way of its impact on technological progress, industrial structure, export, economic scale, environmental pollution, factor allocation, domestic investment, and so on. Therefore, this study aims to add and contribute to the existing literature by examining the impact of China’s OFDI on the water footprint benefits.

2. Literature Review

2.1. The Impact of OFDI (None Analyzing the Impact of OFDI on the Water Footprint Benefit)

There have been numerous studies concerning the impact of OFDI, primarily concerning its effects on technological progress, industrial structure, and exports. Most of the studies about the effect of OFDI on technological progress show that the major determinant of its effect is the absorptive capacity of a country (region), which is, in turn, determined by several factors such as infrastructure level, degree of openness, R&D investment, intellectual property protection, economic development level, marketization process, human capital, system quality, financial development scale, and environmental regulation (Wu and Li, 2015, Zhao and Li, 2017, Baskaran et al., 2017, Huang and Zhang, 2017, and Gu and Han, 2018) [1,2,3,4,5]. These studies have generally found that the technological progress effects of OFDI are heterogeneous across different regions, different industries, and enterprises with different ownership characteristics. As for the industrial structure effect of OFDI, most studies have found that OFDI has played an optimizing role in the home country’s industrial structure (Pan and Yuan, 2016, Andreff, 2017, Nie, 2017, Zhao and Ye, 2018) [6,7,8,9]. However, there is no clear consensus regarding the industrial structural effect of OFDI. A few scholars have found that OFDI has no significant effect on industrial structure, and has not promoted upgrading of the industrial structure in developing countries (Chen and Zhu, 2015, Mao and Lin, 2018) [10,11]. Some empirical studies found that the industrial structure effect of OFDI exhibits a non-linear pattern (Ja and Han, 2018) [12]. Some scholars further discussed the industrial structure effect of OFDI by region, industry, and type of OFDI (Zhang and Zhao, 2018) [13]. As for the export effect of OFDI, while most of the research results seem to support that OFDI promotes a country’s export growth, some studies find no evident contribution of OFDI to export growth (Jiang and Jiang, 2014, Wang et al., 2016, Liu, 2016) [14,15,16]. For instance, Chen and Xian (2018) believe that, although at the extensive margin, OFDI may have promoted China’s export, at the intensive margin, it may have displaced China’s exports [17]. So far, researchers have conducted numerous studies analyzing the impact of OFDI on export growth in different countries, regions, industries, and different types of OFDI (Gu, 2016, Hu and Ling, 2016, Wang et al., 2017) [18,19,20]. Lastly, there have also been many studies on the impact of OFDI on environmental pollution, employment, domestic investment, resource allocation, industrial agglomeration, income, and global value chain upgrading.

2.2. Water Footprint Measurement and Its Influencing Factors (None Considering OFDI)

Regarding the existing literature on the water footprint measurement, many studies have measured the water footprint at various levels, including the national (regional) level, River Basin level, provincial level, urban level, and agricultural products level. Examples of studies at the national level calculated the water footprint of EU, China, Korea, and Australia, respectively (Vanham and Bidoglio, 2013, Yoo et al., 2015, Ge et al., 2016, Nouri et al., 2019) [21,22,23,24]. Examples of studies at the agricultural product level measured the water footprint of melon, sugar cane, barley, cereals, and animal products, respectively (Castellanos et al., 2016, Babak et al., 2017, Mohammad et al., 2018, Song and Chen, 2019, and Mourad et al., 2019) [25,26,27,28,29]. The water footprint studies in China have primarily calculated water footprints at the River Basin level, the provincial level, and the urban level, such as Deng (2014), Wang and Li (2016), Li (2017), and Feng (2018) that calculated and analyzed the water footprint of Shanghai and Chongqing, Weihe River Basin, the urban agglomeration in the middle reaches of the Yangtze River, Shanxi, and other places, respectively [30,31,32,33]. Regarding the literature on the influencing factors of the water footprint, scholars have found various factors affecting the water footprint, including economic development level, industrial structure, urbanization, technological innovation, consumption level, water resources endowment, water use efficiency, human capital, foreign trade, and climate conditions (Zhang et al., 2015, Paolo et al., 2016, Ali et al., 2016, Yang et al., 2016, Zhu et al., 2016, Zhang et al., 2019, Xie et al., 2019, and Wang et al., 2019) [34,35,36,37,38,39,40,41]. Closely related to this paper is that Kan and Lv (2017, 2018) measured the inward foreign direct investment (IFDI) in China by using the foreign capital dependence for empirical research, and the results showed that China’s IFDI (Inward Foreign Direct Investment) promotes the improvement of water footprint. However, the impact is not statistically significant [42,43]. Lv and Kan (2017) further empirically examined the impact of urbanization on the water footprint benefit [44].
As can be seen from the above brief review, the existing literature has achieved fruitful results in analyzing the impact of OFDI and the water footprint measurement as well as its influencing factors. However, in the numerous studies of the impact of OFDI, none have examined the impact of OFDI on the water footprint benefit. Additionally, in the studies of the influencing factors of the water footprint, none has examined the role of OFDI. In general, OFDI is expected to promote economic growth and increase exports, while the resulting increase in consumption will reduce the social, spatial, and environmental benefits of the water footprint. OFDI is expected to replace domestic investment and increase the social, spatial, and environmental benefits of the water footprint. OFDI is also expected to promote technological progress, improve the efficiency of factor allocation, optimize the industrial structure, and the resulting increase in income will enhance the economic benefits of the water footprint. Accordingly, we propose to bridge the gap in the literature in the following ways: (1) Based on a cross-country dynamic panel data from 2003 to 2016, we examine the impact of China’s OFDI on the water footprint benefit using the system generalized method of moments (system GMM). (2) We further analyze the impact of different types of OFDI on water footprint benefits.

3. Methods

3.1. Model Construction

Based on the relevant previous studies, we construct the following empirical model, specifying water footprint benefit (WFX) as the dependent variable, outward foreign direct investment (OFDI) as the focus independent variable, and other related control variables including economic scale (ES), industrial structure (IS), technological progress (TE), environmental regulation (EI), water resources endowment (WB), residents’ income level (PI), urbanization level (UR), import and export trade (TR), foreign direct investment (FO), and climate factors (QH).
ln W F X t = C + γ ln W F X t − 1 + β 1 ln O F D I i t + λ X t + μ i + φ t + ε i t
In this model, t represents the year (the sample period is 2003–2016) and i denotes the country or region of China’s OFDI (the sample countries are 56). (To achieve sample representativeness for each type of OFDI, we selected countries with large and continuous OFDI traffic. The resulting sample consists of the top 20 countries in previous years, excluding the British Virgin Islands, Cayman Islands, Bermuda Islands, Hong Kong, Macao, and Taiwan. The final sample countries for market-seeking OFDI include Philippines, Malaysia, Pakistan, Thailand, Laos, Sri Lanka, Brunei, Myanmar, Bangladesh, Cambodia, and Vietnam. The resource-seeking OFDI sample countries include Saudi Arabia, Australia, Russia, South Africa, Iran, Venezuela, Mongolia, Brazil, Kazakhstan, and Algeria. Lastly, technology-seeking OFDI sample countries include the United States, 27 EU countries, Japan, Canada, New Zealand, Korea, Singapore, Norway, and Switzerland), X, μ, φ, and ε are control variables, cross sectional dummy variables, time series dummy variables, and random error terms, respectively. Due to the inherent inertia (or lingering effect) of the change in the water footprint benefit, the change in the water footprint benefit is likely to have a lag effect. Hence, a lag term is added, which also enables taking into account other factors that are not explicitly included in the model. In addition, considering the possible heteroscedasticity of macroeconomic variables, all variables are included in the model in logarithmic form.

3.2. Variable Measurement and Data Collection

As defined in the above, water footprint benefits include the social benefit of the water footprint (water footprint consumed by unit population, reflecting the capacity of water resources to support population), the economic benefit of the water footprint (water footprint consumed by unit economic output, reflecting economic output brought by water resource consumption), and the spatial benefit of the water footprint (water footprint consumed by unit space, reflecting the amount of water resources consumed in the land area), environmental benefit of the water footprint (the proportion of wastewater discharged from the consumed water footprint, reflecting the capability of clean utilization of water resources). The water footprint/population (the per capita water footprint), water footprint/GDP (water footprint intensity), water footprint/land area (water footprint land density), and waste water/water footprint (water footprint abandonment rate) are used to measure the social, economic, spatial, and environmental benefits of the water footprint, respectively. The water footprint used in the calculation is the sum of freshwater water footprint (EF1) and water pollution footprint (EF2) (Freshwater includes agricultural water, industrial water, household livelihood water, ecological environment water, and virtual water. The virtual water is calculated by the production tree method, following the research of Hoekstra and Chapagain (2007), Deng (2015) [45,46]. The data of agricultural water, industrial water, household livelihood water and the ecological environment water are from China Water Resources Bulletin and China Statistical Yearbook).The formula of the freshwater water footprint is EF1 = N × Wf = aw × Ai/Pw, where N, Wf, aw, Ai, and Pw are, respectively, the total population, the per capita freshwater footprint, the equilibrium factor of water resources, the amount of water resources used in a given category, and the average production capacity of global water resources. The water pollution footprint is calculated by using the formula EF2 = b × F/Pw based on the theory of “grey water,” where b and F are the multiple factor of water resources and the total discharge of waste water, respectively (The equilibrium factor of water resources is set at 5.19, the average production capacity of global water resources is 3140 m3/hm2, and the multiple factor of water resources is 4, following the study of Yu et al. (2014) [47]). Relevant original data are derived from the China Water Resources Bulletin, China Statistical Yearbook.
The focus explanatory variable OFDI is measured by OFDI flows, which are derived from China’s Statistical Bulletin of Outward Foreign Direct Investment over the years. Lastly, the variables GDP (Gross Domestic Product), output value of secondary and tertiary industries/GDP, labor productivity, investment in waste water treatment/industrial waste water discharge, per capita water resources, the population weighted sum of urban residents’ disposable income, rural residents’ per capita net income, the urban population/total population, total foreign trade/GDP, realized foreign direct investment/GDP, and precipitation are used to measure, respectively, economic scale, industrial structure, technological progress (Most literature use government fiscal R&D (Research and Development) investment expenditures or Solow residual method to measure technological progress. The former may underestimate China’s technological progress. The latter method requires many preconditions and assumptions, but China almost does not meet these conditions. Therefore, this paper uses labor productivity as a proxy measure of technological progress), environmental regulation, water resources endowment, income level of residents, urbanization level, import and export trade, IFDI (Inward Foreign Direct Investment), and climate factors (The nominal GDP data are converted to real GDP at the 2003 constant price, according to the GDP index). The original data are obtained from the China Statistical Yearbook, China Industrial Economic Statistical Yearbook, China Industrial Statistical Yearbook, China Environmental Yearbook, China Environmental Statistical Yearbook, CEIC China Economic Database, and the China Economic Network (Most of the data sources report identical values for variables used in this paper. A few inconsistencies arise when the data published in the China Statistical Yearbook differ from those published by other specialized professional data sources. But the differences, if any, are very small, especially after taking logarithms of the slightly different values. We found no statistically significant differences (at a 10% level) after taking logarithmic transformation of the non-identical values). A linear trend interpolation method is used to fill in some missing values to complete the panel data.

3.3. Diagnostic Tests and the System GMM Method

Before estimating the model, a few diagnostic tests were conducted. First, correlation tests were carried out to detect possible pseudo-regression, Unit root test results showed that each variable was a first-order mono-integer I (1), and co-integration test results shown in Table 1 indicated that there was a long-term relationship between variables. Since several control variables are included in the model, there could be multi-collinearity problems. Therefore, the correlation tests between the independent variables were conducted. Given that the correlation coefficients between variables are relatively low and the variance expansion factor is less than 10, we can conclude that the estimation model does not pose a serious multi-collinearity problem. However, there are potential endogenous problems that can cause unreliable regression results. The endogeneity issue could arise from possible impact of the water footprint benefit on the control variables. For example, it is possible that improving the water footprint benefit can stimulate economic growth and further urbanization. It is also possible that the water footprint benefit has changed before OFDI starts to affect the water footprint benefit. To address the potential endogeneity bias, researchers commonly used the GMM method. According to Arellano and Bond (1991), Arellano and Bover (1995), Blundell and Bond (1998) [48,49,50], the GMM method can be divided into the differential GMM method and the system GMM. The estimator of the system GMM method further uses the moment condition of the level equation on the basis of the estimator of the differential GMM method, and takes the first-order difference of the lagged variable as the instrumental variable for the corresponding level variable in the level equation. Therefore, the system GMM method is used in this study to estimate the model. The results shown in Table 2 indicate that the Sargan test statistics are not abnormal, and the Arellano-Bond AR (2) values suggest that the residual has no second-order autocorrelation.

4. Results

4.1. The Impact of OFDI on the Water Footprint Benefit

As shown in Table 2, a 1% increase in OFDI caused per capita water footprint to increase by 0.097%, water footprint intensity to decrease by 0.048%, water footprint land density to increase by 0.106%, and water footprint abandonment rate to increase by 0.063%. All of these effects are statistically significant, which indicates that OFDI has a negative impact on the ability of water resources to support the population, by increasing the economic output brought by water resources consumption, and by increasing the amount of water resources consumed in space, and not conducive to improving the capability of clean utilization of water resources. Thus, OFDI is not conducive for the improvement of social, spatial, and environmental benefits of China’s water footprint, but conducive to the improvement of economic benefits of the water footprint.

4.2. The Impact of Different Types of OFDI on the Water Footprint Benefit

Next, we examine the impact of OFDI on the water footprint benefit by types (market-seeking, resource-seeking, and technology-seeking OFDI). The results are shown in Table 3. In addition, we examine separately the impact of path-wise OFDI or inverse OFDI on the water footprint benefit, with the results shown in Table 4 (Path-wise OFDI refers to China’s investment in other developing countries, while inverse OFDI refers to China’s investment in newly industrialized economies and developed countries).
Table 3 shows that a 1% increase in the market-seeking OFDI would cause the per capita water footprint to increase by 0.126%, the water footprint intensity to decrease by 0.021%, the water footprint land density to increase by 0.139%, and the water footprint abandonment rate to decrease by 0.034%. The estimated coefficient of water footprint intensity of OFDI is not significant. The results indicate that market-seeking OFDI has a negative impact on the ability of water resources to support the population, fails to significantly improve the economic output brought by water resources consumption, increases the amount of water resources consumed in space, and is conducive to the improvement of clean utilization of water resources. Essentially, market-seeking OFDI is not conducive for the improvement of social and spatial benefits of China’s water footprint and has not significantly improved the water footprint economic benefits, but it helps improve the environmental benefits of the water footprint.
Table 3 also shows that a 1% increase in the resource- seeking OFDI would cause the per capita water footprint, the water footprint land density, and the water footprint abandonment rate to increase by 0.076%, 0.069%, and 0.085%, respectively, and also cause the water footprint intensity to decrease by 0.027% (although, insignificantly). The results indicate that resource-seeking OFDI is not conducive for improving the capacity of water resources to support the population, and it increases water resource consumption in land areas. Additionally, it appears that the resource-seeking OFDI is not conducive for the improvement of clean utilization capability of water resources, and does not significantly improve the economic output of water resources’ consumption. Essentially, resource-seeking OFDI is not conducive for the improvement of social, spatial, and environmental benefits of China’s water footprint, and has not significantly improved the economic benefits of the water footprint.
Lastly, Table 3 shows that, when the technology-seeking OFDI increases by 1%, the per capita water footprint, water footprint intensity, water footprint land density, and water footprint abandonment rate decrease by 0.113%, 0.095%, 0.079%, and 0.042%, respectively. This means that technology-seeking OFDI improves the ability of water resources to support the population and the economic output of water resources consumption, reduces the spatial water resources consumption, and is conducive for the improvement of clean utilization of water resources. In other words, technology-seeking OFDI is conducive to the improvement of social, spatial, environmental, and economic benefits of China’s water footprint.
Table 4 shows that, when the path-wise OFDI increased by 1%, the per capita water footprint, water footprint land density, and the water footprint abandonment rate increased by 0.139%, 0.117%, and 0.078%, respectively, and the water footprint intensity decreased (although statistically insignificant) by 0.036%. The results indicate that the path-wise OFDI is not conducive to the improvement of social, spatial, and environmental benefits of China’s water footprint, and has not significantly improved the economic benefits of the water footprint. Table 4 also shows that when the inverse OFDI increased by 1%, the per capita water footprint, water footprint intensity, water footprint land density, and water footprint abandonment rate decreased by 0.085%, 0.074%, 0.062%, and 0.038%, respectively. This means that the inverse OFDI is conducive to the improvement of social, spatial, environmental, and economic benefits of China’s water footprint.

5. Discussion

First, OFDI is not conducive to the improvement of social, spatial, and environmental benefits of China’s water footprint, but conducive to the improvement of economic benefits of the water footprint. The pathways for these impacts of OFDI are that OFDI improves the per capita water footprint and the water footprint land density through (1) an economic scale effect, (2) an export growth effect, (3) domestic investment effect, and (4) consumption effect. It reduces the water footprint intensity through (5) a technological progress effect, (6) a factor allocation effect, (7) an industrial structural effect, and (8) an income effect. It increases the water footprint abandonment rate through (9) the environmental pollution effect and (4) the consumption effect. More specifically, these nine pathway effects are discussed in detail as follows.
(1)
Economic scale effect: OFDI results in growth and expansion of China’s economic scale, which consumes a large amount of water resources, and, hence, increases the per capita water footprint and the water footprint land density. This is achieved by acquiring key resources, advancing the technology level, expanding the international market demand, promoting exports, increasing employment, and upgrading the industrial structure.
(2)
Export growth effect: OFDI drives the export of China’s capital goods and intermediate products (such as machinery and equipment). OFDI also improves the export competitiveness of Chinese enterprises by promoting R&D innovation and improving the import quality of intermediate products, which improves the export capability of enterprises, and, thus, results in phenomenal export growth. Since the bulk of China’s exports are mainly labor-intensive products that are more water-consuming, export growth leads to increase the per capita water footprint and water footprint land density.
(3)
Domestic investment effect: While OFDI may crowd out domestic investment in the short term, in the long run, it may increase domestic investment through correlation linkage and spillover effect, which enriches domestic capital investment channels, and raises the efficiency of domestic capital allocation. Additionally, repatriated profits from OFDI can be directed to capital investment in domestic projects. These investment projects consume plenty of water resources in the construction process and subsequent operation, which raises the per capita water footprint and the water footprint land density.
(4)
Consumption effect: By promoting China’s economic growth, OFDI has increased consumption demand, which results in an increase in the per capita water footprint, the water footprint land density, and the water footprint abandonment rate.
(5)
Technological progress effect: OFDI helps to acquire advanced R&D resources of the host country, learn and utilize advanced technology of the host country, and promote China’s technological progress through the reverse feedback of OFDI R&D achievements. Through overseas M&A (Mergers and Acquisitions), the core technology of the invested enterprises can be obtained directly and then internalized. Along with M&A integration, the tacit knowledge of the host country’s enterprises can also be acquired to promote a technological progress. Moreover, through overseas M&A, establishment of overseas R&D institutes and green land investment, the industrial chain with advanced technology and high R&D level can be embedded in the host country. Through the linkage effect with its upstream and downstream industries, the technological level in China can be further enhanced. In the end, the resulting technological progress can help enterprises improve water use efficiency and reduce the water footprint intensity.
(6)
Factor allocation effect: OFDI promotes transnational flow of China’s capital and labor, and optimizes the allocation of factors worldwide. It improves the efficiency of factor allocation and promotes factor agglomeration, which can bring forth scale economies and improvement of labor productivity. All this contributes to the improvement of water use efficiency and reduces the water footprint intensity.
(7)
Industrial structure effect: OFDI promotes the optimization and adjustment of China’s industrial structure. This is achieved by acquiring key resources and advanced technology, bypassing trade barriers, alleviating overcapacity, transferring marginal industries, supporting emerging industries, linking upstream and downstream industries, and forming industrial agglomeration. The industrial structure shifts toward increasing the proportion of middle and high-end manufacturing industry and modern service industry. These industries consume less water resources. Hence, the intensity of the water footprint has been reduced.
(8)
Income effect: The learning effect and reverse technology spillover effect of OFDI have promoted China’s technological progress, helped raise the productivity level, and, thus, raised China’s wage level. At the same time, OFDI helps make full use of the international market, realize economies of scale, improve corporate profitability, and again raise the wage income of Chinese employees. Increase in income, in turn, has promoted the upgrading of the consumption structure and transformation of the consumption mode toward healthy consumption, which increased the demand for grain products that are low calorie, fat and sugar, and less water-consuming (such as rice, potatoes, barley, broad beans, and wheat), and reduced the demand for high water-consuming meat products (According to the existing research, 10,000–15,000 kg of water is needed to produce 1 kg meat (its effective utilization rate is less than 0.01%) and 400–3000 kg of water is needed to produce 1 kg cereals, which is about 5% of the water needed to produce meat). From Table 2, we can see that the estimated coefficient of the impact of OFDI on water footprint intensity is relatively small. This small effect may be understood as follows. Although OFDI promotes China’s technological progress, due to the weak domestic absorptive capacity and the rebound effect of water resources in technological progress itself, the effect of technological progress on the reduction of water footprint intensity in China is relatively limited. Although OFDI promotes the upgrading of China’s industrial structure, it plays a limited role in increasing the proportion of high-end manufacturing and modern service industries, which tend to have less water consumption. Although OFDI has increased the income level of Chinese residents, it has increased the income of highly skilled workers disproportionately. The widening income gap slowed the upgrading of consumption structure toward a healthy consumption concept and mode, and, thus, makes the effect of income increase on the reduction of water footprint intensity not pronounced.
(9)
Environment pollution effect: OFDI promotes China’s non-intensive economic growth, which sharply increases the export scale of labor-intensive products. This generates water pollution and raises the water footprint abandonment rate.
Second, the market-seeking OFDI is not conducive to the improvement of social and spatial benefits of China’s water footprint and has not significantly improved the water footprint economic benefits, but it helps improve the environmental benefits of the water footprint. These results can be explained by considering several pathway effects of OFDI alluded to in the preceding section. This occurs through economic scale expansion, export growth, associated increases in domestic investment and consumption expansion, the market-seeking OFDI improved per capita water footprint, and the land density of the water footprint. In addition, through industrial structure upgrading and increased efficiency in factor allocation, market-seeking OFDI reduced the water footprint intensity. However, market-seeking OFDI has not reduced the water footprint intensity through a technology progress effect and income effect. On the contrary, the income effect of market-seeking OFDI actually increased the water footprint intensity. The main reason is that market-seeking overseas investment mainly focused on labor-intensive manufacturing enterprises. Due to the fierce competition in the global market and the weak bargaining power, these enterprises are vulnerable to capture effect and restricted to increase income of low-skilled workers in China. In other words, some overseas investment enterprises need to reduce labor costs to enhance their global marketing competitiveness, which further results in the weakening of bargaining power of Chinese employees and suppresses the increase of income. This, in turn, hampered the upgrading of the consumption structure, and the change in the consumption concept and mode toward healthy and less water-intensive consumption. Hence, the income effect is not conducive to the reduction of water footprint intensity, offsetting the technological progress effect, and making the impact of market-seeking OFDI on the water footprint intensity insignificant. Lastly, although market-seeking OFDI improves the water footprint abandonment rate through the consumption effect, it made some high-polluting industries incur loss and exit the market. It also caused overcapacity and marginal industries to transfer to other countries, which results in reductions in water pollution and the water footprint abandonment rate.
Third, the resource-seeking OFDI is not conducive to the improvement of social, spatial, and environmental benefits of China’s water footprint, and has not significantly improved the economic benefits of the water footprint. Reasons for these results are similar to those mentioned in explaining the impact of market-seeking OFDI. Through economic scale expansion, export growth, increase in investment, and consumption expansion, resource-seeking OFDI improves the per capita water footprint and water footprint land density. Resource-seeking OFDI did not significantly improve factor allocation efficiency, increase residents’ income, and optimize industrial structure and promote technological upgrading, in order to have a significant effect in reducing water footprint intensity. In addition, resource-seeking OFDI leads to the increase in the water footprint abandonment rate through its effect on environmental pollution and consumption.
Fourth, the technology-seeking OFDI is conducive to the improvement of social, spatial, environmental, and economic benefits of China’s water footprint. Reasons for these results are that technology-seeking OFDI promotes the transformation of economic growth mode and export mode, increases domestic investment with high technology content, promotes the optimization of consumption structure, and improves the qualities of economic growth, export, domestic investment, and consumption. As a result, technology-seeking OFDI reduced the per capita water footprint and land density of the water footprint through economic scale effect, export growth effect, domestic investment effect, and consumption effect. It has also significantly promoted technological progress, industrial structure upgrading, factor allocation efficiency, and domestic wage level. Thus, technology-seeking OFDI has also significantly reduced the water footprint intensity through a technological progress effect, industrial structure effect, factor allocation effect, and income effect, and has reduced the water footprint abandonment rate by reducing water pollution and increasing consumption of modern products and services that generate less sewage.
Fifth, the path-wise OFDI is not conducive to the improvement of social, spatial, and environmental benefits of China’s water footprint, and has not significantly improved the economic benefits of the water footprint. Since the path-wise OFDI is China’s investment in other developing countries, mainly for obtaining resources and exploiting overseas markets, the impacts of market-seeking and resource-seeking OFDI discussed earlier would carry over. Therefore, path-wise OFDI would improve the per capita water footprint and water footprint land density through the economic scale effect, the export growth effect, the domestic investment effect, and the consumption effect. The path-wise OFDI has no significant effect on reducing the water footprint intensity through a factor allocation effect, an income effect, an industrial structure effect, and a technological progress effect. It improves the water footprint abandonment rate through an environmental pollution effect and a consumption effect.
Sixth, the inverse OFDI is conducive to the improvement of social, spatial, environmental, and economic benefits of China’s water footprint. Since the inverse OFDI is China’s investment in newly industrialized economies and developed countries, mainly to acquire technology and expand global markets, the impact of technology-seeking OFDI discussed earlier would carry over. Therefore, inverse OFDI would promote the transformation of the economic growth mode and export growth mode, improve the quality of domestic investment, optimize the consumption structure, and, hence, would reduce the per capita water footprint and land density of the water footprint through an economic scale effect, an export growth effect, a domestic investment effect, and a consumption effect. In addition, as is the case of technology-seeking OFDI, inverse OFDI can significantly reduce the water footprint intensity through a technological progress effect, an industrial structure effect, a factor allocation effect, and an income effect, and it can reduce the water footprint abandonment rate by reducing water pollution and reducing the consumption of traditional products and services that generate more sewage.

6. Conclusions

How to enhance the water footprint benefit in conjunction with OFDI is of great significance to reconcile the contradiction between supply and demand of water resources. Based on a dynamic panel data, the impact of OFDI on the water footprint benefit is examined using the method of system GMM. The results showed that, as OFDI increased by 1%, the water footprint per capita increased by 0.097%, the water footprint intensity decreased by 0.048%, the land density of the water footprint increased by 0.106%, and the water footprint abandonment rate increased by 0.063%. This means that OFDI was not conducive to the improvement of social, spatial, and environmental benefits of China’s water footprint, but was conducive for the improvement of water footprint economic benefits.
In terms of different types of OFDI, the results reveal that market-seeking and resource-seeking OFDI increase the water footprint per capita and the land density of the water footprint in China, but not significantly reduce the intensity of the water footprint. Additionally, market-seeking OFDI reduces the water footprint abandonment rate, whereas resource-seeking OFDI increases the water footprint abandonment rate. The technology-seeking OFDI reduces the water footprint per capita, the water footprint intensity, the water footprint land density, and the water footprint abandonment rate. These results mean that market-seeking and resource-seeking OFDI are not conducive to the promotion of social and spatial benefits of China’s water footprint, and have not significantly improved economic benefits of the water footprint. Market-seeking OFDI is conducive to improving environmental benefits of the water footprint, whereas resource-seeking OFDI is not conducive to improving environmental benefits of the water footprint. Additionally, technology-seeking OFDI is conducive to the social, economic, spatial, and environmental benefits of China’s water footprint. Furthermore, path-wise OFDI increases the water footprint per capita, the water footprint land density, and the water footprint abandonment rate, and reduces (although insignificantly) the water footprint intensity. This means that path-wise OFDI is not conducive for the improvement of social, spatial, and environmental benefits of China’s water footprint, and has not significantly improved the economic benefits of China’s water footprint. Lastly, inverse OFDI reduces the per capita water footprint, the water footprint intensity, the water footprint land density, and the water footprint abandonment rate. Therefore, it is conducive to China’s water footprint social, economic, spatial, and environmental benefits.
The findings from this study have relevant policy implications and can help provide some policy prescriptions for an economy such as China to engage in OFDI and achieve enhancement of water footprint benefits. For instance, in addition to expanding market-seeking and resource-seeking OFDI, China should actively increase the scale of technology-seeking OFDI. Additionally, while continuing to expand path-wise OFDI, China should further increase the scale of inverse OFDI. By taking advantage of the complementary and synergetic effects of different types of OFDI, an economy can capture the whole effects of OFDI to reap the water footprint’s full social, economic, spatial, and environmental benefits.

Author Contributions

D.K. analyzed the data and drafted the manuscript. W.H. gave many valuable comments on the draft and also polished it.

Funding

The National Natural Science Foundation of China (No. 71764018), Social Science Foundation of Jiangxi Province (No. 18JL03), National Statistical Science Foundation of China (No. 2018LZ01), Foundation of Jiangxi Provincial Soft Science Research Base of Water Safety and Sustainable Development (No. 19JDYB03), Natural Science Foundation of Jiangxi Province (No. 20171BA A218014), Science and Technology Foundation of Jiangxi Education Department (No. GJJ171001), and the Humanities and Social Sciences Foundation of Jiangxi Province (No. JJ17119) supported and funded this study.

Acknowledgments

The authors greatly appreciate the funding support. The authors would also like to extend special thanks to the editor and anonymous reviewers for their constructive comments and suggestions for improving the quality of this paper.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table
Table 1. Cointegration test results of panel data.
Table 1. Cointegration test results of panel data.
Test MethodPer Capita Water FootprintWater Footprint IntensityWater Footprint Land DensityWater Footprint Abandonment Rate
PedroniPanel-v−0.372 (0.009)−0.460 (0.007)−0.314 (0.011)−0.499 (0.005)
Panel-ρ−3.574 (0.005)−4.389 (0.000)−2.930 (0.007)−4.662 (0.000)
Panel-PP−8.326 (0.000)−7.204(0.000)−6.812 (0.000)−9.838 (0.000)
Panel-ADF−5.725 (0.000)−6.021 (0.000)−4.688 (0.000)−7.457 (0.000)
Group-ρ−4.517 (0.000)−5.522 (0.000)−4.686 (0.000)−5.865 (0.000)
Group-PP−6.789 (0.000)−9.308 (0.000)−8.823 (0.000)−7.042 (0.000)
Group-ADF−5.094 (0.000)−6.257 (0.000)−4.175 (0.000)−6.681 (0.000)
KaoADF−4.258 (0.000)−5.453 (0.000)−6.491 (0.000)−5.950 (0.000)
Note: The p-value for each corresponding test statistic is displayed in parenthesis.
Table
Table 2. Estimation results of the impact of OFDI (Outward Foreign Direct Investment) on the water footprint benefit.
Table 2. Estimation results of the impact of OFDI (Outward Foreign Direct Investment) on the water footprint benefit.
Per Capita Water FootprintWater Footprint IntensityWater Footprint Land DensityWater Footprint Abandonment Rate
C4.238 **3.1422.963 *2.191
Dependent variable lagged one period0.273 *0.234 **0.215 **0.267 **
lnOFDI0.097 **−0.048 *0.106 **0.063 **
lnES0.226 ***0.195 **0.262 **0.224 ***
lnIS−0.092−0.084−0.081−0.073
lnTE−0.104 **−0.090 **−0.093 *−0.081 *
lnEI−0.075 *−0.063 **−0.070 **−0.059 *
lnWB0.120 *0.087 *0.119 **0.086 **
lnPI−0.051−0.046−0.053−0.045
lnUR0.140 **0.124 **0.151 ***0.132 **
lnTR0.164 **0.151 *0.177 *0.165 *
lnFO0.0490.0320.0540.038
lnQH0.093 *0.089 **0.102 *0.090 **
Wald test1184.3921010.4861133.758967.287
Sargan test0.2730.2680.2620.256
Arellano-Bond AR (1)0.0050.0050.0040.004
Arellano-Bond AR (2)0.2580.2500.2470.239
Note: *, ** and *** indicate that the variable is significant at the level of 10%, 5%, and 1%, respectively.
Table
Table 3. Effects of different types of OFDI on water footprint benefits (1).
Table 3. Effects of different types of OFDI on water footprint benefits (1).
Market-Seeking OFDIResource-Seeking OFDITechnology-Seeking OFDI
Per Capita Water FootprintWater Footprint IntensityWater Footprint Land DensityWater Footprint Abandonment RatePer Capita Water FootprintWater Footprint IntensityWater Footprint Land DensityWater Footprint Abandonment RatePer Capita Water FootprintWater Footprint IntensityWater Footprint Land DensityWater Footprint Abandonment Rate
C2.255 **2.302 **3.9471.8233.996 *2.9632.794 *2.0664.8193.572 **3.3652.491
Dependent variable lagged by one period0.2070.275 *0.206 **0.219 *0.2570.221 **0.203 **0.252 *0.264 *0.2270.208*0.256 **
lnOFDI0.123 **−0.0210.139 *−0.034 **0.076 **−0.0270.069 **0.085 *−0.113 **−0.095 *−0.079 **−0.042 **
lnES0.216 *0.196 **0.250 **0.215 ***0.191 *0.164 **0.221 *0.187 **0.231 ***0.199 **0.267 **0.228 *
lnIS−0.088−0.080−0.074−0.070−0.077−0.069−0.067−0.061−0.112−0.103−0.096−0.089
lnTE−0.095 ***−0.084 **−0.089 **−0.077 *−0.087 **−0.075 ***−0.078 **−0.068 ***−0.127 **−0.110 **−0.114 ***−0.096 ***
lnEI−0.072 **−0.060 *−0.067 **−0.056 **−0.063 **−0.053 *−0.050 **−0.049 **−0.092 *−0.077 **−0.086 *−0.072 **
lnWB0.114 **0.083 **0.103 *0.082 **0.126 *0.092 **0.125 *0.091 **0.107 **0.076 *0.103 **0.074 **
lnPI−0.049−0.044−0.051−0.043−0.039−0.048−0.056−0.047−0.060−0.054−0.062−0.057
lnUR0.134 *0.118 **0.144 **0.126 *0.148 **0.131 **0.159 **0.139 *0.121 **0.108 *0.127 **0.115 **
lnTR0.156 **0.142 *0.169 *0.157 **0.173 **0.159 *0.185 *0.174 **0.138 *0.131 **0.154 **0.143 *
lnFO0.0470.0310.0520.0350.0620.0340.0570.0400.0430.0280.0460.039
lnQH0.089 *0.085 *0.097 **0.076 **0.098 *0.093 **0.108 *0.095 *0.081 **0.079 *0.090 *0.078 **
Wald test1129.910964.0041081.605922.7921248.3491065.1721194.9811019.5231028.052877.136984.391839.605
Sargan test0.2610.2560.2500.2430.2840.2810.2760.2680.2370.2330.2270.222
Arellano-Bond AR (1)0.0050.0050.0040.0040.0050.0050.0040.0040.0040.0040.0030.003
Arellano-Bond AR (2)0.2460.2390.2360.2280.2720.2630.2600.2520.2230.2170.2140.207
Note: *, ** and *** indicate that the variable is significant at the level of 10%, 5%, and 1%, respectively.
Table
Table 4. Effects of different types of OFDI on water footprint benefits (2).
Table 4. Effects of different types of OFDI on water footprint benefits (2).
Path-Wise OFDIInverse OFDI
Per Capita Water FootprintWater Footprint IntensityWater Footprint Land DensityWater Footprint Abandonment RatePer Capita Water FootprintWater Footprint IntensityWater Footprint Land DensityWater Footprint Abandonment Rate
C3.463 **2.917 *3.7352.155 **4.589 **3.4013.2042.372 *
Dependent variable lagged by one period0.257 *0.275 **0.224 *0.2610.252 *0.216 **0.198 *0.243
lnOFDI0.139 **−0.0360.117 **0.078 **−0.085 *−0.074 *−0.062 **−0.038 *
lnES0.225 **0.199 ***0.261 **0.214 **0.220 **0.189 **0.254 **0.217 **
lnIS−0.091−0.083−0.078−0.073−0.107−0.094−0.091−0.085
lnTE−0.104 **−0.088 **−0.093 *−0.080 ***−0.121 **−0.105 ***−0.109 **−0.091 ***
lnEI−0.075 *−0.063 **−0.065 **−0.058 **−0.086 *−0.073 *−0.082 *−0.069 **
lnWB0.133 **0.097 *0.126 **0.096 ***0.102 **0.072 **0.098 **0.065 *
lnPI−0.049−0.051−0.059−0.050−0.057−0.051−0.059−0.054
lnUR0.156 **0.135 **0.168 ***0.147 *0.115 **0.103 *0.121 ***0.110 *
lnTR0.182 **0.167 **0.196 *0.180 **0.129 *0.125 **0.147 *0.136 **
lnFO0.0600.0360.0590.0420.0410.0270.0440.037
lnQH0.104 *0.099 **0.114 **0.095 *0.077 **0.075 **0.086 *0.074 **
Wald test1317.5551124.1641261.2271076.043978.918835.209937.337799.472
Sargan test0.3020.2970.2910.2810.2260.2220.2160.211
Arellano-Bond AR (1)0.0060.0060.0040.0040.0040.0040.0030.003
Arellano-Bond AR (2)0.2870.2780.2750.2660.2120.2070.2040.197
Note: *, ** and *** indicate that the variable is significant at the level of 10%, 5%, and 1%, respectively.

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KAN, D.; HUANG, W. Empirical Study of the Impact of Outward Foreign Direct Investment on Water Footprint Benefit in China. Sustainability 2019, 11, 4409. https://doi.org/10.3390/su11164409

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KAN D, HUANG W. Empirical Study of the Impact of Outward Foreign Direct Investment on Water Footprint Benefit in China. Sustainability. 2019; 11(16):4409. https://doi.org/10.3390/su11164409

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KAN, Daxue, and Weichiao HUANG. 2019. "Empirical Study of the Impact of Outward Foreign Direct Investment on Water Footprint Benefit in China" Sustainability 11, no. 16: 4409. https://doi.org/10.3390/su11164409

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