Hydropower has been the main source of energy in Nigeria, until recently when thermal and fossil-fuel driven turbines and other alternatives are becoming commonplace. The concern for power generation tends to becloud environmental and natural resource degradation that accompanies execution of hydropower projects or plants. One of the principal resources concerned is the watershed of hosting rivers. These impacts reflect on fishery, navigation, domestic and agricultural water supplies. This study examined the impacts of hydropower plants on watershed of Jebba Lake on Niger River and evaluates the environmental cost of that impact on the various facets of the watershed. It also assessed the efficacy of remediation in respect of hydropower plants hosting communities. Contingent valuation method was adopted through a survey of local communities and the Jebba lake of Niger River watershed, Jebba, Nigeria. Stratified samples were drawn from fishers, farmers and dwellers of neighboring communities. Data were tabulated and percentages, mean scores, variances and standard deviations were computed. The hypotheses were tested using ANOVA, F-statistics and the t-test. Results shows that beyond the marketed cost of producing power there are myriads of environmental costs, often concealed by the difficulty of determining the non-market values of the benefits and cost associated therewith. It was concluded that the environmental impact of hydropower plants/projects is significant and calls for critical study during its environmental administration process. Thus, the total cost of producing electricity should reflect environmental components in order to serve as adequate basis for pricing units of production.
Background to the study
The need for increased energy generation is global. Statistics abound to justify increased energy demand, considering population growth and growth in economic activities generally. The projected population growth rate for the world for 2009 to 2035 was put at 0.9%, with 1.1% growth rate for the periods 2009 to 2020 and a slower growth rate of 0.8% between 2020 and 2035. In Africa, a growth rate of 2.3% was projected for 2009 to 2020 which is expected to slow down to 2% between 2020 and 2035 to average 2.1% overall for 2009 to 2035 (OECD/IEA, 2011). Nigeria’s population is estimated at 173.6 million (2013) and growing at the rate of 2.7% annually (World Bank Group, 2015). Similarly, economic activities had risen over the years, with global GDP growth and Nigeria’s economic growth at 6.3% between 2011 and 2015. This economic growth has brought with it increased economic activities that require energy consumption. The estimated Total Global Primary Energy Supply in 2012 was estimated by The International Energy Agency (IEA) at 155,505 terawatt-hour (TWh) or 17.7 TW (Mtoe 13,371); up from 71,013 terawatt-hour (TWh) (Mtoe 6,106) in 1973 over a 100% increase (OECD/IEA, 2014). As observed by Kaunda et al. (2012), “the global energy is still dominated by fossil fuel,” providing about 80% of total energy supply. The environmental implications of fossil fuel paints a gloomy future for the world, hence the search for a more sustainable energy source.
Sustainable energy system is one that extracts, converts and utilizes energy in a manner that its current generation does not lead to significant environmental degradation, and its use does not compromise those of future generations in meeting their needs (Kaunda et al., 2012). Environmental degradation and climate change has occupied the focus of the world considering the threat to livelihoods and biodiversity, especially food diversity and security. The looming consequences of global climate change have created a strong imperative to move away from fossil fuels and to develop more sources of renewable energy. This had encouraged the adoption of renewable sources that is carbon neutral and creates less air pollution. Hydroelectric power is one of such sources. It is a renewable energy source. Hydropower is one of the important renewable energy resources for generating electricity and hydropower occupies global position in sustainable energy generation.
A discussion on global environment and climate change is crucial because they constitute the main concerns for energy systems. It was noted by Ebinger and Vergara (2011),that energy sector emits about 70% of the total Green House Gases (GHG) emissions with electricity generation being responsible for a larger share of global energy consumption. But, hydroelectricity generation technology seems to resolve the problem of GHG and in addition is one of the cheapest in terms of electricity generation costs (USA Department of Energy, 2012). Hydroelectric power systems are judged to be highly efficient in energy conversion- mechanical work is directly converted into electricity. This technology may achieve 85% efficiency as contrasted with thermal-electric plants which achieve less than 50% on the average (Roth, 2005).
Wang et al. (2009) observed that “although hydropower is usually regarded as a kind of clean energy, its negative impacts on water quality, estuary sedimentation, habitat, landscape, biodiversity and human health during development are generally well known and critically studied” (Puff et al., 1997; Jansson et al., 2000; WCD, 2000; Andreas et al., 2002; Gehrke et al., 2002; Dudgeon, 2005). They further noted that hydropower development has many negative impacts on watershed ecosystems. Determining the costs of hydropower plants on the environment, especially the livelihoods of downstream communities and their economies, may not be so easily determined because environmental degradation cannot be so easily quantified and valued. To successfully estimate the costs of environmental impacts requires measurement of the impact in terms of its occurrence or the probability of occurrence; and, developing valuation bases for the measured impacts. The procedure involved is akin to what is often adopted in cost-benefit studies with the adoption of various methods such as the contingent valuation like Willingness to Pay or Accept Compensation, the Travel Cost and some Hedonic measures. Such hypothetical values are subjected to empirical analysis and the mean values are adopted for the population targeted. The extrapolated values have significant effects on policy in respect of the project and the pricing of services provided.
The problem
The significance of hydropower projects in curtailing climate change cannot be overlooked, in that when compared to other sources of electrical power it is one of the least direct contributors to climate change. However, when examined closely, a hydropower project produces social and environmental impacts during construction and operation phases of the project. The construction of the plant could involve making of roads, dam, weirs, tunnels, power plants structures, and electricity transmission lines. Often, land is cleared, forest removed and some communities displaced to make room for such constructions. Flooding of land by the reservoir may disrupt ecosystem, destroy infrastructure, and displace settlements.
The various activities involved in construction and operating a hydropower project “result in localized air and water pollution, loss of biodiversity, destruction of infrastructure, change of landscape, destruction of settlements, and loss of livelihood and cultural identity in the direct project affected areas” (Kaunda et al., 2012). There is a consensus of opinions as to the degradation of the environment and livelihoods around the projects, especially downstream, what constitutes problem in literature is how to ascribe meaningful values to these impacts in a manner that would be acceptable globally. This gap is a formidable challenge in research on natural assets accounting and management. It is the thrust of this study. In resolving this issue, the questions were:
1. What is the nature of impact of hydropower plant operations in Jebba on the watershed?
2. To what extent has hydropower plant dam affected the ecosystem downstream?
3. What is the perceived cost of hydropower plant to communities?
Arising from these questions, the main objective of this study was to evaluate the costs of Jebba lake hydropower plant on the Niger River watershed of Jebba. Accordingly, it was aimed to:
1. Determine the nature of impacts of Jebba hydropower plant on the Niger River watershed of Jebba;
2. Assess the impacts of the dam on ecosystem downstream;
3. To evaluate the costs of hydropower plant on communities.
This study was able to apply the contingent valuation method, through the Willingness to Accept Compensation, to provide a pointer to metrics to consider in such valuation process.
For the first part of evaluation, that is Willingness to Pay, the Independent Variables (Respondents Attributes) were: X1: Gender; X2: Marital Status; X3: State of Origin; X4: Education; X5: Size of farm;
X6: Occupation; X7: Average Annual Income; X8: Age; X9: Size of family; X10: Location; X11: Distance from Power Plant; And the dependent variables are the benefits provided by power plants. These are: ENG- Energy; EMP- Employment; COL- Collaborations; COM- Commerce; IRR- Irrigation; FCM- Flood Control Mechanism.
For the second part of evaluation, that is, Willingness to Accept Compensation, the Independent Variables (Respondents Attributes) were: X1: Gender; X2: Marital Status; X3: State of Origin; X4: Education; X5: Size of Farm; X6: Occupation; X7: Average Annual Income; X8: Age; X9: Size of Family; X10: Location; X11: Distance from Power Plant; And the dependent variables are the benefits provided by power plants. These are: FLD- Flooding; WPL- Water Pollution; FSD- Fish Diversity loss; FSQ- Fish Size and Quantity Loss; VFD- Vegetables and Fruits Diversity loss; GLL- Grazing Land loss; WLD- Wildlife loss; RCL- Riparian Crops loss; FOR- Forest Cover loss; ERS- Erosion; YLD- Lowered Crop Yield
Questions raised elicited a dichotomous response of Yes or No, in respect of the willingness of respondents to pay or accept compensation for environmental benefits and damages of hydropower plant as identified. That is, to each of the identified environmental benefit and costs, the respondents indicated their willingness to pay as shown in Tables 1 and 2 and Figures 1 and 2.
Analysis of data
The data in respect of the dichotomous responses on environmental services were analysed with the use of LOGIT Regression Model. However, to overcome the problems of crowding out of important details in the analysis, each response was subjected to the evaluation, using the model as follows:
Where; X1 = Gender of respondents; X2 = Marital Status of respondents; X3 = State of origin of respondents; X4 = Education of respondents; Xs = Size of farm of respondents; X6 = Annual Income of respondents; X7 = Age of respondents; X8 = Size of family of respondents; X9 = Distance from Power Plant. Fi, could be F1, or ENG- Energy; F2, or EMP- Employment;`F3, or COL- Collaborations for Development; F4, or COM- Improved Commerce; F5, or IRR- Irrigation; F6, or FCM- Flood Control Mechanism. Mi, could be M1, or FLD- Flooding; M2, or WPL- Water pollution; M3, or FSD- Fish diversity loss; M4,or FSQ- Fish size and quantity loss; M5 or VFD- Vegetables and fruits diversity loss; M6 or GLL- Grazing land loss; M7 or WLD- Wildlife loss; M8 or RCL- Riparian crops loss; M9 or FOR- Forest cover loss; M10 or ERS- Erosion; and M11 or YLD- Lowered crop yield. The results of the LOGIT regression are shown for each of the environmental variable.
Willingness to Pay (WTP)
Constant and free power supply
The equation line for determining the probability and significance of the WTP for ENG, the outcome variable, z, is the willingness to pay for provision of constant and free power supply. As stated earlier, the independent variables are X1 to X9. Thus, the expanded equation is given as:
That is =f (-1.44X1-0.63X2 +0.36X3 - 0.36X4 +1.68X5 -2.07X6 +0.87X7 - 1.44X8 + 1.44X9 + 1.76).
The P values and odds ratio are given in Appendix Table 1. The combined influence of the nine variables to determine the willingness to pay for constant and free power supply was not significant at P= 0.3510 which is substantially greater than 0.05, or 0.10 significance levels. This is further proved by a mere 8.36% Pseudo R2. The only variables that were significant were X6, that is, Annual Income (at 5%) and X5, that is, Size of Farm (at 10%).
EMP- Employment
The equation line for determining the probability and level of significance of the WTP for EMP. The outcome variable, z, is the willingness to pay for Provision of Employment for indigenes. As stated earlier, the independent variables are X1 to X9. Thus, the expanded equation is given as:
That is = f (1.99X1 + 0.74X2 - 0.36X3 - 0.45X4 -0.72X5 + 0.63X6 - 0.19X7 - 1.45X8 + 0.32X9 +3.01)
The P values and odds ratio are given in Appendix Table 2. The combined influence of the nine variables to determine the willingness to pay for provision of employment to indigenes was not significant at P= 0.2442 which is substantially greater than 0.05, or 0.10 significance levels. This is further proved by a mere 9.41% Pseudo R2. The only variable that was significant was X1, that is, Gender (at 5% level of significance).
COL- Collaborations for Development
The equation line is uused for determining the probability and significance of the WTP for COL. The outcome variable, z, is the willingness to pay for Collaborations for Development. As stated earlier, the independent variables are X1 to X9. Thus, the expanded equation is given as:
That is = f (-3.55X1 +1.24X2 - 0.21X3 - 3.71X4 + 0.61X5 + 1.15X6 - 0.35X7 + 0.45X8 + 4.11X9 + 3.01).
The P values and odds ratio are given in Appendix Table 3. The combined influence of the nine variables to determine the willingness to pay for watershed and prevention of water pollutions was significant at P = 0.0000 which is less than 0.05, or 0.10 significance levels. This is further proved by a 25.2% Pseudo R2. Three variables exerted significant influence in the respondents’ choice. These were X1, that is, Gender; X4, Education; and, X9, Distance from Forest Reserve (at 5% level of significance).
COM- Improved Commerce
The equation line is used for determining the probability and significance of the WTP for COM. The outcome variable, z, is the willingness to pay for Wildlife Conservation. As stated earlier, the independent variables are X1 to X9. Thus, the expanded equation is given as:
That is =f (-2.11X1 +0.35X2 +1.76X3 –0.01X4 +3.09X5 +1.66X6 + 0.52X7 - 0.06X8 +0.25X9 +1.28).
The P values and odds ratio are given in Appendix Table 4. The combined influence of the nine variables to determine the willingness to pay for wildlife conservation was significant at P= 0.0002 which is less than 0.05, or 0.10 significance levels. This is further proved by a 17.82% Pseudo R2. Four variables exerted significant influence on the respondents’ choice. These were X1, that is, Gender; X5, Size of farm (at 5% level of significance); and, X3, State of origin; and X6, Annual Income (at 10% level of significance).
IRR- Irrigation
The equation line is used for determining the probability and significance of the WTP for IRR. The outcome variable, z, is the willingness to pay for Irrigation. As stated earlier, the independent variables are X1 to X9. Thus, the expanded equation is given as:
That is = f (-1.71X1 + 1.58X2 - 0.51X3 - 3.16X4 + 1.78X5 + 2.27X6 - 1.13X7 - 2.10X8 + 0.69X9 + 2.02)
The P values and odds ratio are given in Appendix Table 5. The combined influence of the nine variables to determine the willingness to pay for maintenance of carbon balance was significant at P= 0.0017 which is less than 0.05, or 0.10 significance levels. This is further proved by a 17.82% Pseudo R2. Five variables exerted significant influence on the respondents choice, namely, X4, that is, Education; X6, Annual Income; X8 , Size of family (at 5% level of significance) and, X1, Gender; and X5, Size of Farm (at 10% level of significance).
FCM- Flood Control Mechanism
The equation line is used for determining the probability and significance of the WTP for FCM. The outcome variable, z, is the willingness to pay for Flood Control Mechanism. As stated earlier, the independent variables are X1 to X9. Thus, the expanded equation is given as:
That is = f (-1.63X1 + 1.72X2 + 0.14X3 - 2.55X4 + 0.93X5 + 2.48X6 - 1.42X7 - 2.12X8 + 0.51X9 + 2.24).
The P values and odds ratio are given in Appendix Table 6. The combined influence of the nine variables to determine the willingness to pay for biodiversity was significant at P= 0.0017 which is less than 0.05, or 0.10 significance levels. Four variables exerted significant influence on the respondents choice, namely, X4, that is, Education; X6, Annual Income; X8 , Size of family (at 5% level of significance) and, X2, Marital Status.
Willingness to accept compensation (WTA)
FLD- Flooding
The equation line is used for determining the probability and significance of the WTA for FLD. The outcome variable, z, is the Willingness to Accept Compensation for Flooding. As stated earlier, the independent variables are X1 to X9. Thus, the expanded equation is given as:
That is =f (0.54X1 +1.97X2 -0.41X3 +0.51X4 -0.05X5 +0.81X6 - 1.26X7 +0.55X8 +1.08X9 +0.96). The P values and odds ratio are given in Appendix Table 7. The combined influence of the nine variables to determine the willingness to accept compensation for flooding was not significant at P = 0.2823 which is less than 0.05, or 0.10 significance levels. This is further proved by a 5.39% Pseudo R2. One variable, X2, Marital Status exerted significant influence on the respondents’ choice (at 5% level of significance).
WPL- Water Pollution
The equation line is used for determining the probability and significance of the WTP for WPL. The outcome variable, z, is the Willingness to Accept Compensation for Water Pollution. As stated earlier, the independent variables are X1 to X9. Thus, the expanded equation is given as:
That is =f (-0.45X1 + 2.31X2 +1.09X3 - 2.32X4 + 0.96X5 + 2.70X6 - 2.33X7 -2.09X8 + 0.78X9 + 1.60). The P values and odds ratio are given in Appendix Table 8. The combined influence of the nine variables to determine the willingness to Compensation for Water Pollution was significant at P = 0.0001 which is less than 0.05, or 0.10 significance levels. Five variables exerted significant influence on the respondents choice, namely, X4, that is, Education; X5, Size of Farm; X6, Annual Income; and, X7 , Age (at 5% level of significance).
FSD- Fish Diversity Loss
The equation line is used for determining the probability and significance of the WTP for FSD. The outcome variable, z, is the willingness to pay for Fish Diversity Loss. As stated earlier, the independent variables are X1 to X9. Thus, the expanded equation is given as:
The P values and odds ratio are given in Appendix Table 9. The combined influence of the nine variables to determine the willingness to Accept Compensation for Fish Diversity Loss loss was significant at P= 0.0000 which is less than 0.05, or 0.10 significance levels. This is further proved by a 21.43% Pseudo R2. Eight variables exerted significant influence on the respondents choice, namely, X1, Gender; X4, Education; X6, Annual Income; X7, Age: X9 , Distance from forest reserve (at 5% level of significance) and, X2, Marital Status; and X3, State of Origin, and X5, Size of Farm (at 10% level of significance).
FSQ - Fish Size and Quantity Loss
The equation line is used for determining the probability and significance of the WTP for FSQ. The outcome variable, z, is the willingness to Accept Compensation for Fish Size and Quantity loss. As stated earlier, the independent variables are X1 to X9. Thus, the expanded equation is given as:
That is = f (0.90X1 + 0.08X3 + 0.48X4+0.77X5 - 0.90X6 + 0.51X7 - 0.60X8 + 0.32X9 + 1.08).
The P values and odds ratio are given in Appendix Table 10. The combined influence of the nine variables to determine the willingness to Accept Compensation for Fish Size and Quantity loss willingness to Accept Compensation for Fish Size and Quantity loss was not significant at P= 0.2857 which is much greater than 0.05, or 0.10 significance levels. This is further proved by a 3.65% Pseudo R2. None of the variables exerted significant influence on the respondents choice, at 5 and 10% levels of significance).
VFD- Vegetables and Fruits Diversity Loss
The equation line for determining the probability and level of significance of the WTP for VFD. The outcome variable, z, is the willingness to accept compensation for Vegetables and Fruits Diversity loss. As stated earlier, the independent variables are X1 to X9. Thus, the expanded equation is given as:
That is = f (1.99X1 + 0.74X2 - 0.36X3 - 0.45X4 - 0.72X5 + 0.63X6 - 0.19X7 - 1.45X8 + 0.32X9 + 3.01).
The P values and odds ratio are given in Appendix Table 11. The combined influence of the nine variables to determine the willingness to accept compensation for Vegetables and Fruits Diversity loss was not significant at P = 0.2442 which is substantially greater than 0.05, or 0.10 significance levels. This is further proved by a mere 9.41% Pseudo R2. The only variable that was significant was X1, that is, Gender (at 5% level of significance).
GLL- Grazing Land loss
The equation line is used for determining the probability and significance of the WTA for GLL. The outcome variable, z, is the Willingness to Accept Compensation for Grazing Land Loss. As stated earlier, the independent variables are X1 to X9. Thus, the expanded equation is given as:
That is =f (0.54X1 +1.97X2 -0.41X3 +0.51X4 -0.05X5 +0.81X6 - 1.26X7 +0.55X8 +1.08X9 +0.96).
The P values and odds ratio are given in Appendix Table 12. The combined influence of the nine variables to determine the willingness to Accept Compensation for Grazing Land Loss was not significant at P= 0.2823 which is less than 0.05, or 0.10 significance levels. This is further proved by a 5.39% Pseudo R2. One variable, X2, Marital Status exerted significant influence on the respondents choice (at 5% level of significance).
WLD- Wildlife Loss
The equation line is used for determining the probability and significance of the WTP for WLD. The outcome variable, z, is the willingness to accept compensation for wildlife loss. As stated earlier, the independent variables are X1 to X9. Thus, the expanded equation is given as:
That is =f (-1.71X1 +1.58X2 -0.51X3 –3.16X4 +1.78X5 +2.27X6 - 1.13X7 -2.10X8 +0.69X9 +2.02). The P values and odds ratio are given in Appendix Table 13. The combined influence of the nine variables to determine the willingness to accept compensation for wildlife loss was significant at P= 0.0017 which is less than 0.05, or 0.10 significance levels. This is further proved by a 17.82% Pseudo R2. Five variables exerted significant influence on the respondents choice, namely, X4, that is, Education; X6, Annual Income; X8 , Size of family (at 5% level of significance); X1, Gender; and X5, Size of Farm (at 10% level of significance).
RCL- Riparian Crops Loss
The equation line is used for determining the probability and significance of the WTP for RCL. The outcome variable, z, is the willingness to accept compensation for riparian crops loss. As stated earlier, the independent variables are X1 to X9. Thus, the expanded equation is given as:
That is = f (-2.11X1 + 0.35X2 + 1.76X3 - 0.01X4 + 3.09X5 + 1.66X6 + 0.52X7 - 0.06X8 + 0.25X9 + 1.28).
The P values and odds ratio are given in Appendix Table 14. The combined influence of the nine variables to determine the willingness to accept compensation for riparian crops loss was significant at P = 0.0002 which is less than 0.05 or 0.10 significance levels. This is further proved by a 17.82% Pseudo R2. Four variables exerted significant influence on the respondents’ choice. These were X1, that is, Gender; X5, Size of farm (at 5% level of significance); and, X3, State of origin; and X6, Annual Income (at 10% level of significance).
FOR- Forest Cover Loss
The equation line is used for determining the probability and level of significance of the WTP for FOR. The outcome variable, z, is the willingness to accept compensation for forest cover loss. As stated earlier, the independent variables are X1 to X9. Thus, the expanded equation is given as:
That is =f (1.99X1 +0.74X2 - 0.36X3 - 0.45X4 - 0.72X5 + 0.63X6 - 0.19X7 - 1.45X8 + 0.32X9 + 3.01).
The P values and odds ratio are given in Appendix Table 15. The combined influence of the nine variables to determine the willingness to accept compensation for forest cover loss was not significant at P = 0.2442 which is substantially greater than 0.05 or 0.10 significance levels. This is further proved by a mere 9.41% Pseudo R2. The only variable that was significant was X1, that is, Gender (at 5% level of significance).
ERS- Erosion
The equation line is used for determining the probability and significance of the WTP for WPL. The outcome variable, z, is the Willingness to Accept Compensation for Erosion. As stated earlier, the independent variables are X1 to X9. Thus, the expanded equation is given as:
That is =f (-0.45X1 +2.31X2 +1.09X3 –2.32X4 +0.96X5 +2.70X6 - 2.33X7 -2.09X8 +0.78X9 +1.60).
The P values and odds ratio are given in Appendix Table 16. The combined influence of the nine variables to determine the willingness to accept compensation for erosion was significant at P= 0.0001 which is less than 0.05 or 0.10 significance levels. Five variables exerted significant influence on the respondents choice, namely, X4, that is, Education; X5, Size of Farm; X6, Annual Income; and X7 , Age (at 5% level of significance).
YLD- Lowered Crop Yield
The equation line is used for determining the probability and significance of the WTA for YLD. The outcome variable, z, is the willingness to pay for Lowered Crop Yield. As stated earlier, the independent variables are X1 to X9. Thus, the expanded equation is given as:
That is = f (0.90X1 + 0.08X3 + 0.48X4 + 0.77X5 - 0.90X6 + 0.51X7 - 0.60X8 + 0.32X9 + 1.08).
The P values and odds ratio are given in Appendix Table 17. The combined influence of the nine variables to determine the willingness to accept compensation for lowered crop yield was not significant at p = 0.2857 which is much greater than 0.05, or 0.10 significance levels. This is further proved by a 3.65% Pseudo R2. None of the variables exerted significant influence on the respondents’ choice, at 5 and 10% levels of significance).
Assigning Values to Environmental impacts of Hydropower Plant on the Watershed of Jebba Lake on Niger River, Jebba- Nigeria
The data in respect of amounts which the respondents are willing to pay for each of the environmental services were in the intervals of Below N1,000; Between N1,000 and 10,000; Between N10,000 and 20,000; Above N20,000. The average WTP for each of the environmental benefits are given in Table 3.
These costs are per capita values of the unit of power produced. To arrive at total environmental costs of hydropower generation, these unit costs need to be extrapolated to reflect production levels from time to time.
