Estimating the Province-specific Environmental Kuznets Curve in China: A Geographically Weighted Regression Approach

Transcription

1 Estimating the Province-specific Environmental Kuznets Curve in China: A Geographically Weighted Regression Approach Yoomi Kim *, Katsuya Tanaka **, and Chazhong Ge *** Abstract This study reconsiders the Environmental Kuznets Curve (EKC) relationship using the Geographically Weighted Regression (GWR) model. This approach can explore spatial non-stationary: the model can be fitted at each point in data, weighting all observations by a function of distance from regression point. Thus, this approach allows parameter estimates to vary spatially. Using this approach, the EKC relationship can be analyzed region specifically while taking spatial heterogeneity into account, rather than describing average relationship over the entire study area. In this study, the GWR model is empirically applied to SO2 emission, water discharge, and solid waste production in 29 provinces or cities of China during We develop three GWR models to estimate an existence of EKC in each of three environmental performance indicators. We also investigate the position of GRP per population after the analysis period on EKC of each province or city by applying 2012 data. Our results provide strong evidences that the GWR model is valid in explaining relationships between environmental performance and economic growth in China more effectively. Locallyregressed GWR models outperform globally regressed ordinary least squares (OLS) model in all three pollutants. The GWR model estimates existence of EKC relationships in SO2, waste water, and solid waste in many of 29 provinces or cities in China, and spatial variations in EKC relationships vary among pollutants. Key Words China ; Environmental Kuznets Curve; Geographically Weighted Regression; solid waste production; SO2 emission; water discharge * Research Fellow in the Seoul National University Asia Center, Seoul National University. kimyoomi@snu.ac.kr ** Associate Professor in the Research Center for Sustainability and Environment, Shiga University. tanakak@biwako.shiga-u.ac.jp *** Fellow in the Chinese Academy for Environmental Planning. gecz@caep.org.cn 1

2 1. Introduction China has sustained remarkably rapid economic growth over the last decades. Per capita GDP increased from 381 yuan in 1978, when China declared its economic reform, to 41,908 yuan in This is primarily due to gradually conducted open market policies and subsequent expansion of industrial sector. Meanwhile, however, such rapid industrialization has induced serious pollution problems. Future trend of both economic and environmental performance is of great concern in China. To address relationship between economic growth and environment, a number of studies have investigated an existence of Environmental Kuznets Curve (EKC) in China (for example, De Groot, Withagen, and Minliang (2004); He (2006); Shen (2006); Tao, Zheng, and Lianjun (2008); Diao, Zeng, Tam, and Tam (2009); Shu, Fantang, Huaiyang, and Zhencheng (2012); Song, Zhang, and Wang, (2013)). Many of these studies have analyzed EKC relationships in various pollutants such as SO2 emission and waste water discharges from industrial sources. For example, Shen (2006) validated the EKC in SO2 emissions and three water pollutants (Arsenic, Cadmium, and COD). De Groot, Withagen, and Minliang (2004) found that the EKC exists in industrial waste gas and solid waste at intermediate levels of economic development. In recent studies, Song, Zhang, and Wang (2013) demonstrated that some areas, such as Beijing, Guizhou, Jilin, Shanghai, and Tibet already reach their inflection points of the EKC in the case of industrial waste gas emission, while Anhui, Fujian, Hainan, Liaoning, and Qinghai demonstrate no inflection point. However, there is a concern that selection of pollutants and methods have a decisive effect on the results (Brajer, Mead, and Xiao, 2011). One major limitation of existing EKC studies is that most of them depend on global regression models. In other words, previous studies evaluate an average relationship between environmental quality and economic development in a research area. An average relationship from a global regression model, however, may not be representative of the situation in any particular part of study region, especially in a case of a highly diverse area. Furthermore, estimated average relationships may overlook or underestimate significant and important local relationships between environment and economic growth. In addition, it might well be that there are contrasting relationships in different parts of China which 1 These values are in 2010 constant prices. The rate of exchange in 2010 was approximately 1 US dollars = 6.77 Yuan. Indices of GDP per capita of the year of 2013 is 1,837.5 if the index of the year of 1978 is

3 tend to cancel each other out in the calculation of the global parameter estimate (Fotheringham, Brunsdon, and Charlton, 2002). Thus, it may be far more informative to produce a set of local statistics by applying the locally-weighted regression approach and to map estimated values than simply to rely on the assumption that a single global estimate will be an accurate representation of all parts of the study area. In the field of environmental economics, the Geographically Weighted Regression (GWR), one of locally-weighted regression models, has been gaining greater attention in several areas including the Hedonic Pricing Model (for example, see Cho, Bowker, and Park (2006)). However, to our knowledge, no study has applied the GWR or any other locally-weighted models to EKC relationships. In this regard, the primary objective of this study is to investigate the EKC relationship in each province or city of China using locally-weighted regression approach, GWR. Environmental performances are measured by three different pollutants from industrial sources: those include SO2 emission, waste water discharge, and solid waste production. In addition, this study investigates the impacts of other socio-economic factors (e.g. population density and geographical factor) on pollutants above. These objectives are achieved by applying the GWR model to 29 provinces or cities in China (figure 1) during This paper is organized as follows. The next section briefly introduces the GWR model and data used for empirical estimations. Section three reports and interprets the estimated results from the three GWR models of each pollutant. The last section summarizes and concludes this study with policy implications of empirical results. 2. The Model 2.1. Geographically Weighted Regression This section briefly describes the basics of GWR model and its estimation. Consider first a global regression model, y = β + β x + ε (1) it 0 k itk it k where y it denotes an observation of the ith point in space at period t. x itk and ε it represent the kth regressor and disturbance term with conventional assumptions, 3

4 respectively. The GWR approach extends this traditional regression framework by allowing local rather than global coefficients to be estimated. Thus, the GWR model can be defined as follows, yit = β0 ( ui, vi ) + βk ( ui, vi ) xitk + εit (2) k where ( ui, v i) denotes the coordinates of the ith point in space and ( ui, vi) realization of the continuous function β ( uv, ) at point i. That is, we allow a k β is a continuous surface of parameter values, and measurements of this surface are taken at certain points to denote the spatial variability of the surface (Fotheringham, Brunsdon, and Charlton, 2002). Calibration of the locally weighted regression model follows a local weighted least squares approach. Unlike OLS, the locally weighted regression assigns weights based on their spatial proximity to location i in order to account for the fact that an observation near location i has more of an influence in the estimation of the β ( uv, ) than do observations located farther from i. That is, 1 ' ( ) ( ) ' ( ) ( ) ˆ β u, v = XW u, v X XW u, v Y (3) i i i i i i k where, ˆβ represents an estimate of β, X is a vector of the variables of structural, neighborhood, and location characteristics x ik, Y is a vector of y i, W( ui, v i) is an nxn diagonal matrix with diagonal elements w ii denoting the geographical weighting of observed data point for location i. To better understand how locally weighted regression operates, consider a locally weighted regression equivalent of the classical regression equation, Y = ( β X)1+ ε (4) where is a logical multiplication operator in which each element of β is multiplied by a corresponding element of X, and 1 is a conformable vector of 1 s. If there are n data points and k explanatory variables including a constant term, both β and X will have dimensions nxk. The matrix β now consists of n sets of local parameters and has 4

5 the following structure: β ( u, v ) ( u, v ) k ( u, v ) ( u, v ) ( u, v ) ( u, v ) β β β β β β β β β k 2 2 = ( u, v ) ( u, v ) ( u, v ) 0 n n 1 n n k n n (5) W( i ) is an nxn spatial weighting matrix of the form: ( ) W i wi w win i2 = (6) where w ij is the weight given to data point j in the calibration of the model for location i. The diagonal elements of the weight matrix, w ij, are equal to: w ij ( dij b) if dij < b = 0 otherwise (7) where d ij is the Euclidean distance between point i and j, and b is a chosen bandwidth. At the regression point i, the weight of the data point is unity and falls to zero when the distance between i and j equals the bandwidth or higher. As b tends to be infinity, w ij approaches 1 regardless of d ij in which case the parameter estimates become uniform and locally weighted regression is equivalent to OLS. Conversely, as b becomes smaller, the parameter estimates will increasingly depend on observations in close proximity to location i and hence have increased variance. A cross-validation (CV) approach is suggested for local regression for a selection of optimal bandwidth (Cleveland, 1979). CV takes the following form: n ˆ ( ) 2 i i (8) CV = y y b i= 1 5

6 where yˆ i is the fitted value of y i with the observations for point i omitted from the fitting process. The bandwidth is chosen to minimize CV. Thus, in the local weighted regression model, only houses up to the optimal level of b are assigned nonzero weights for the nearest neighbors of census-block group i. The weight of these points will decrease with their distance from the regression point. Sensitivity analysis can be conducted for bandwidths of plus and minus 50% of the b selected by the CV approach. Further details about GWR models can be found in Fotheringham, Brunsdon, and Charlton (2002), LeSage (2004), and Wheeler (2014). Because the local model allows regression coefficients to vary across space, the spatially varying relationships between environmental performance and their determinants can be estimated locally. This allows us to quantify the impacts of economic development and other factors on environmental performance at provincial level in China. Furthermore, spatially varying parameters and elasticities can be displayed in spatial maps using Geographic Information System (GIS). These advantages of the GWR model are demonstrated in section Model Specification and Data This study estimates equation (2) in the previous subsection using the following specification, YY iiii = ββ 0 + ββ 1 GRPP iiii + ββ 2 GRPPSQ iiii + ββ 3 POPDENSE iiii + ββ 4 D_COAST iiii +ββ 5 D_TIME iiii + εε iiii (9) where Y it is per capita environmental performance (volume of SO2 emission, waste water discharge, and solid waste production from industrial sector) in province or city i at year t. GRPP and GRPPSQ denote provincial gross regional product per capita and its squared term. Provincial GRP is adjusted by CPI (1993=100). POPDENSE is population density (in person/m 2 ). D_COAST is a dummy variable for coastal regions 2, and D_TIME is a dummy variable that indicates time period after the year of Coastal provinces or cities denote: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Guangxi, and Hainan. 3 Time dummy is needed to be included because there are several changes in the criteria of collecting statistical data in State Statistical Bureau ( ). 6

7 Finally, β s are parameters to be estimated. This study uses data of three pollutants from industrial sources (SO2 emission, waste water discharge, and solid waste production) in 29 provinces and metropolitan cities in China 4. Variables used for estimation of equation (9) are obtained from China Statistical Yearbook (State Statistical Bureau, ) 5. Descriptive statistics of variables used in this study are presented in table Results The estimated coefficients for SO2, waste water, and solid waste models are presented in tables 2, 3, and 4, respectively. Tables indicate local GWR models outperform global OLS models in all three pollutants in terms of Adjusted R 2 and AIC. Thus, model fits are significantly improved by estimating locally rather than globally. The parameter estimates for the seven independent variables vary widely over space. Table 5 which is the result of the test for spatial variability of the GWR coefficients, demonstrates that there is significant spatial variability in terms of model selection criteria. All variables including GRPP and GRPPSQ have negative value of diff- Criterion. This provides strong evidence that the EKC relationships are not constant, but vary among provinces or cities in China. In other words, it is difficult to capture reasonable average relationship by global OLS models in these three models, since global OLS estimates can produce average relationship among all provinces or cities in China. One of the advantages of the GWR model is that estimated results are spatially displayed, based upon resolution of data used in the study. Since this study conducts province or city-level locally regression, all results can be displayed using provincial or municipal data of China and GIS software such as ESRI s ArcGIS. Through this process, Figure 2, 3, and 4 illustrate spatial variations in the EKC relationships in SO2, waste water, and solid waste from industrial sources in China. Highlighted provinces or cities in figures present the areas which have significant the EKC relationship (i.e., β 1 > 0 and β 2 < 0 in equation (9)). It is revealed that there are quite different patterns 4 Tibet is not included in our analysis due to lack of some information from statistical data sources. Chongqing is also not included because it was split from Sichuan during the estimation period (in 1997). To keep consistency of data, we combine Chongqing and Sichuan together and treated them as a single province in this study. 5 GRP per population in 2012 are used in order to grasp the trend of sustainable development after 2010 in Table 6. 7

8 among three pollutants. Figure 2 indicates that 12 out of 29 provinces or cities are demonstrated to have EKC relationships in SO2 emission. This is remarkably different from OLS estimates in table 2 that EKC relationship in SO2 on average is fail to satisfy in entire China. Our results indicate that 12 provinces or cities located in the northern region of China (Beijing, Gansu, Hebei, Henan, Inner Mongolia, Liaoning, Ningxia, Shaanxi, Shandong, Shanxi, Tianjin, and Xinjiang) achieve SO2 emissions reduction with GRP growth, even though economic growth is not likely to mitigate SO2 emissions in other 17 southern and northern west provinces or cities. In other words, more active environmental policies (e.g. environmental standard, environmental management enhancement, etc.) will be needed to induce air quality improvement in these provinces or cities. Figure 3 indicates the EKC relationship in waste water discharge. The figure predicts that there are significant EKC relationships between GRP growth and industrial waste water discharge in 21 out of 29 provinces or cities of China. This suggests that there will be reduce discharge of waste water in these 21 provinces or cities ultimately along with economic growth. However, it is found that 8 provinces or cities including Anhui, Fujian, Jiangsu, Jiangxi, Shanghai, Sichuan, Xinjiang, and Zhejiang, do not have the EKC relationship in waste water discharge. The result of the industrial solid waste production in Figure 4, the EKC relationship holds in 10 northern-central provinces or cities except coastal regions: Beijing, Gansu, Hebei, Henan, Inner Mongolia, Ningxia, Qinghai, Shaanxi, Shanxi, and Tianjin. This result demonstrates a positive tendency that solid waste will be decreased in these 10 provinces or cities as China s economy grow. However, economic growth is not likely to reduce solid waste in most provinces or cities including western, southern and coastal regions. Finally, Table 6 presents the ratio of actual GRP per population to calculated turning point based on the analytical results on the EKC relationships in each provinces or cities. This table presents the position of GRP per population in 2012 on calculated EKC of each province or city. From these estimates, it is possible to identify the trend of sustainable development after the analysis periods 6. Specifically, a ratio over 1.00 indicates that GRP per population reach a calculated turning point. The pollutants have 6 This calculation is assuming that the current pattern of economic growth of provinces or cities of China goes consistent. 8

9 started to decrease after this point along with economic growth, therefore it is possible to grasp the status of pollutants on the EKC of each province or city. In the case of SO2, 12 provinces or cities have the validity of the EKC relationship between GRP and SO2 emissions, but only Beijing and Liaoning passed the inflection points of the EKC. In the industrial solid waste production section, only Beijing reached the calculated turning point with the ratio of Both Beijing and Liaoning are representative developed cities in China that are the first and third ranks of the actual 2012 GRP per population 7. This results may imply that environmental institutions (e.g., local governments and local environmental NGOs) could be established and activated in those developed cities and have sufficient capacity for reducing such pollutants than other regions. In the middle section in Table 6, lots of provinces or cities are verified that they have passed through turning points in the EKC of waste water. More specifically, eight of 21 provinces or cities, which are proved the existence of EKC, have the value over 1.00: Beijing, Guangdong, Guangxi, Hainan, Hunan, Liaoning, Tianjin, and Yunnan. This results demonstrate the unique geographical distribution. Beijing, Liaoning, and Tianjin that are located in the eastern coastal region are first, third, and firth ranks of GRP per population in 2012 respectively. This indicates that, as same as SO2 and solid waste EKC, developed cities with high GRP per capital is likely to control industrial waste water discharge more effectively than other provinces or cities in China. Other five provinces or cities, Guangdong, Guangxi, Hainan, Hunan, and Yunnan, are all located in the southern part of China. Historically, manufacturing industry in China has been developed in the southern region. For example, Guangdong, which has the highest ratio of 2.695, contains one of the leading economic regions of China that is well known as the Golden Delta of Guangdong. Since this region is in contiguity with Hong Kong, there are vigorous interchanges of people, goods or services. In this context, it is expected that precedent growing concern of waste water pollution problems in response to early development of manufacturing in these five provinces or 7 Adjusted GRP per population by CPI (1993=100) of each province or city in 2012 are as follows: Beijing (1.223), Shanghai (1.112), Liaoning (0.794), Tianjin (0.759), Jiangsu (0.708), Zhejiang (0.706), Guangdong (0.609), Xinjiang (0.566), Shandong (0.553), Heilongjiang (0.543), Fujian (0.530), Inner Mongolia (0.530), Hainan (0.513), Jilin (0.512), Ningxia (0.489), Shaanxi (0.482), Shanxi (0.475), Qinghai (0.457), Hebei (0.457), Hubei (0.443), Hunan (0.439), Sichuan (0.399), Henan (0.385), Yunnan (0.343), Guangxi (0.343), Jiangxi (0.338), Gansu (0.324), Anhui (0.324), Guizhou (0.298). 9

10 cities may result in the significant EKC and ratio values over Further analysis will be needed to reveal specific factors causing such different patterns of the EKC. Overall, only Beijing successfully passed through the inflection points of the EKC and enter a phase of improving environmental and economic performance simultaneously. SO2, waste water, and solid waste in Beijing are diminishing, as GRP keeps growing. Beijing has being taken very stringent measures and enforcement environmental policies that are converged on the Green Olympic Movement ever since Beijing has got Olympic sponsorship in Thus, the Green Olympic Movement can be considered as one reason for this result. In contrast, EKC relationships in all pollutants are not significant in Sichuan and six provinces or cities located in south eastern region (Anhui, Fujian, Jiangsu, Jiangxi, Shanghai, and Zhejiang). This result indicates that it is difficult yet to define the relationship between economic growth and environmental pollutants in those provinces or cities. There is possibilities of different shapes not only EKC (i.e. an inverted-ushaped) but also other shapes, such as linear relationship or cubic function graph. Therefore, more in long term analysis will be needed in those provinces or cities to capture graph shapes of the relationship between economic growth and environmental pollutants. 4. Summary and Conclusions This study reconsiders the EKC relationship using the GWR model. This approach can explore spatial non-stationary: the model can be fitted at each point in data, weighting all observations by a function of distance from regression point. Thus, this approach allows parameter estimates to vary spatially. Using this approach, the EKC relationship can be analyzed region specifically while taking spatial heterogeneity into account, rather than describing average relationship over the entire study area. In this study, the GWR model is empirically applied to SO2 emission, water discharge, and solid waste production in 29 provinces or cities of China during We develop three GWR models to estimate an existence of EKC in each of three environmental performance indicators. We also investigate the position of GRP per population after the analysis period on EKC of each province or city by applying 2012 data. Our results provide strong evidences that the GWR model is valid in explaining relationships between environmental performance and economic growth in China more 10

11 effectively than a global OLS model. It is revealed that locally-regressed GWR models outperform globally regressed OLS model in all three pollutants. The GWR model estimates the existence of EKC relationships in SO2, waste water, and solid waste in many of 29 provinces or cities in China, and grasps spatial variations in EKC relationships vary among pollutants. This study is limited because specific factors affecting environmental performance are not fully evaluated. For example, industrial structure, capital intensity, and environmental management capacities are not considered in this analysis. This is mostly due to existence of multicollinearity and data availability. More trials may be needed to find better specifications, in both theoretically and statistically. In addition, spatial variations of estimated results using GWR are displayed in terms of only sign of parameters and significant (figures 2, 3, and 4). Thus, more detailed analysis is required to make further investigation of factors affecting environmental performance and to derive effective policy implications toward balanced economic growth in China. Acknowledgments This work was partially supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF- 2013S1A5B8A ). 11

12 References Brajer, V., Mead, R. W., & Xiao, F Searching for an Environmental Kuznets Curve in China's air pollution. China Economic Review 22(3): Cho, S.H., Bowker, J.M., & Park, W.M Measuring the contribution of water and green space amenities to housing values: An application and comparison of spatially-weighted hedonic models. In American Agricultural Economics Association 2006 Annual Meeting. Cleveland, S.W Robust locally weighted regression and smoothing scatterplots. Journal of American Statistical Association 74: Diao, X. D., Zeng, S. X., Tam, C. M., & Tam, V. W EKC analysis for studying economic growth and environmental quality: A case study in China. Journal of Cleaner Production 17(5): De Groot, H. L., Withagen, C. A., & Minliang, Z Dynamics of China s regional development and pollution: An investigation into the Environmental Kuznets Curve. Environment and Development Economics 9: Fotheringham, A.S., Brunsdon, C. & Charlton, M Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Chichester, West Sussex: John Wiley & Sons. He, J What is the role of economic growth and openness for China s environment? An analysis based on divisia decomposition method from the regional angle. Working paper, Department d Economique, Universite de Sherbrooke LeSage, J.P A family of geographically weighted regression. In L. Anselin, R. J. G. M. Florax, and S. J. Rey, eds. Advances in Spatial Econometrics. London: Springer, pp Shu, L., Fantang, Z., Huaiyang, F., & Zhencheng, X An empirical test of the Environmental Kuznets Curve in Guangdong Province, China. APCBEE Procedia 1: Shen, J A simultaneous estimation of Environmental Kuznets Curve: Evidence from China. China Economic Review 17: Song, M. L., Zhang, W., & Wang, S. H Inflection point of Environmental Kuznets Curve in Mainland China. Energy policy 57: State Statistical Bureau China Statistical Yearbook. Beijing: China Statistical Publishing House. Tao, S., Zheng, T., & Lianjun, T An empirical test of the Environmental Kuznets Curve in China: A panel cointegration approach. China Economic Review 19(3):

13 Wheeler, D. C Geographically weighted regression. In M.M. Fischer, P. Nijkamp (eds.), Handbook of Regional Science (pp ). Springer Berlin Heidelberg. 13

14 Table 1. Descriptive Statistics of Variables Variable Definition Unit Mean S.D. Min. Max. Indso2p Volume of Sulphur Dioxide Emission 10,000 tons Indwwp Volume of wasted water discharged 10,000 tons Indswp Volume of Industrial Solid Wastes Produced 10,000 tons Grpp Gross regional product 100 million yuan Grppsq Gross regional product 100 million yuan Popdens Population density 10,000 person/m Coast dummy Dummy variable for coastal regions Time dummy Dummy variable for year after Note: GRP and expenditure are adjusted by CPI (1993=100). Source: China Statistical Yearbook and China Environmental Yearbook ( ). Table 2. The Model Estimates: Industrial SO2 Emission Variable OLS GWR Coeff. S.E. Min LQ Med UQ Max Intercept *** Grpp * Grppsq Popdens Coast dummy *** Time dummy *** N Adjusted R AIC Note: Dependent variable: Industrial SO 2 per capita Table 3. The Model Estimates: Industrial Waste Water Discharge Variable OLS GWR Coeff. S.E. Min LQ Med UQ Max Intercept *** Grpp *** Grppsq ** Popdens *** Coast dummy *** Time dummy N Adjusted R AIC Note: Dependent variable: Industrial Waste Water Discharge per capita 14

15 Table 4. The Model Estimates: Industrial Solid Waste Production Variable OLS GWR Coeff. S.E. Min LQ Med UQ Max Intercept Grpp *** Grppsq ** Popdens * Coast dummy *** Time dummy *** N Adjusted R AIC Note: Dependent variable: Industrial Solid Waste Production per capita Table 5. Test for Spatial Variability of the GWR Coefficients Variable SO2 Waste water Solid waste F DIFF of Criterion F DIFF of Criterion F DIFF of Criterion Intercept Grpp Grppsq Popdens Coast dummy Time dummy Note: Positive value of diff-criterion (AICc, AIC, BIC/MDL or CV) suggests no spatial variability in terms of model selection criteria. 15

16 Table 6. Ratio of Actual GRP per Population to Calculated Turning Point Province or SO2 Waste water Solid waste city name Turning point Ratio Turning point Ratio Turning point Ratio Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang Note 1: The turning of each province or city indicates the calculated inflection points of EKC from the analysis in this study. Note 2: The equation to calculate the ratio of actual GRP per population to turning point is 1+(GRPP GRPP_OPT) / GRPP_OPT. Thus, ratio 1.00 indicates that the current GRP per population in 2012 reach the calculated turning point. Note 3: Only provinces or cities that have significant EKC relations are calculated. 16

17 Figure 1. Provinces and Major Cities in China 17

18 Figure 2. Spatial Variations in SO2 EKC Relationships 18

19 Figure 3. Spatial Variations in Waste Water EKC Relationships 19

20 Figure 4. Spatial Variations in Solid Waste EKC Relationships 20