Distribution of proposed amounts in the sample.
\r\n\t
\r\n\tMoreover, the application of modern methods in the developing world will help attract new investments, whether domestic or international from developed countries to developing countries. These actions will create new jobs for young people in terms of exploration projects and thus increase the living standard of people living in poor countries and help increase national income.
\r\n\t
\r\n\tWith regard to the problem of desert reclamation in these countries, the results of the application of these methods can help in finding new groundwater sites that can be used for drinking and agriculture, and create new urban communities.
\r\n\t
\r\n\tFinally, it can be concluded that these methods play an important and essential role in combating poverty in developing countries in the world. At the end, the book chapters will help the readers to know about Geophysics and it's applications.
Semiconducting metal oxides have brought incredible attention as chemical sensors due to its characteristic resistivity and sensitivity changing features in an ambient environment [1, 2]. Being an N-type semiconductor, ZnO has been extensively used as a gas-sensing material owing to its wide band gap of 3.37 eV and high exciton binding energy of 60 meV at ambient temperature [3, 4, 5, 6, 7, 8, 9]. It has drawn tremendous attention in the last few decades due to its specific electrical, catalytic, and photochemical optoelectronic properties. It is the most widely studied semiconductor in gas-sensing applications because of its significant response to different reducing or oxidizing gases such as carbon monoxide, hydrogen, ammonia, ethanol, and acetone [10, 11, 12]. But some intrinsic disadvantages including high operating temperature, slow response and recovery time, and less sensitivity hinder the further advance in the development of ZnO-based gas sensors. So, boundless efforts have been made in the last few years to overcome these limitations employing different methods, including noble metal doping, structure optimization, and heterostructure fabrication [13, 14, 15].
\nOne such method to enhance gas-sensing properties is doping impurity elements [16]. Among the available methods for synthesizing nanostructures, electrospinning is a simple, adaptable, and low-cost technique for fabricating organic and inorganic nanofibers with significant lengths, uniform diameters, and various compositions [17]. During the electrospinning process, high voltage is applied to a polymer droplet being suspended at the tip of a syringe needle [18]. In this we present electrospun CoO-ZnO composite nanofibers with different concentrations like 1, 3, and 5 wt% of cobalt oxide. Calcination of cobalt oxide and zinc oxides leads to the formation of CoO-ZnO composite nanofibers having tunable diameters and significant porous structures. We studied the properties of the CoO-ZnO composite nanofibers fabricated, and specific dopants are used to improve the selectivity. The sensitivity of the nanofibers over acetone gas at lower concentrations has been examined, and the outcome of the fabricated nanofiber for sensing the acetone levels in breath of diabetic patients is sensed in low concentrations.
\nAlali et al. successfully synthesized ZnO/CoNiO2 hollow nanofibers by an electrospinning method and postcalcination treatment. ZnO/CoNio2 hollow nanofibers gave an excellent response to ammonia solution, and further a great enhancement was achieved in ammonia sensing with these hollow fiber structures. ZnO/CoNio2 offered the best choice of ammonia-sensing materials by providing a response of 40 at 80°C [19]. Sun et al. synthesized pure and Er-doped ZnO nanofibers by electrospinning for high sensitivity detection of ethanol. They demonstrated that the diameter of the ZnO nanofibers decreases from 200 to 70 nm with the increase of Er content and the Er doping significantly increased the ethanol-sensing sensitivity of ZnO nanofibers at an optimum operating temperature of 240°C. This research also found that at the optimal Er content of 1.0 at%, the sensitivity of the nanofibers is 3.7 times larger than that of pure ones [20]. Wang et al. synthesized pure and Cu-doped ZnO fibers through electrospinning technology. The results of this research revealed that H2S gas-sensing properties of ZnO nanofibers were effectively improved by Cu doping: 6 at% Cu-doped ZnO nanofibers showed a maximum sensitivity to H2S gas, and the response to 10 ppm H2S is one order of magnitude higher than the one of pure ZnO nanofibers [21]. ZnO nanofibers were fabricated by an electrospinning method using a solution containing sol-gel precursors and solvent. It had been observed that the crystallinity of ZnO nanofibers improved with the increase in annealing temperature. The diameters of ZnO nanofibers after annealing above 600°C ranged from 35 to 100 nm. This work also confirmed the fact that the activation energy of ZnO nanofibers for electrical conduction was inversely proportional to the annealing temperature, and they also showed CO gas-sensing capacity at concentration as low as 1.9 ppm [22]. One-dimensional ZnO-SnO2 hollow nanofibers were synthesized by Shouli bhai et al. by one-step electrospinning method and annealing treatment. The research depicted that the 20 atm% Zn composite not only exhibits the highest response that is 9 and 5.2 times higher than that of pristine ZnO and SnO2, respectively. It also exhibited rapid response, excellent selectivity, and stability at low operating temperature of 90°C, thereby promising to be a sensing material for the detection of NO2 [23]. Pure and Ag-doped ZnO-SnO2 hollow nanofibers had been synthesized through electrospinning method by Ma et al. The sensor based on the Ag-doped ZnO-SnO2 hollow nanofibers exhibited excellent gas-sensing performance at low operating temperature at 200°C and the fast response and recovery characteristics at low concentration of 1 ppm. This demonstrated that Ag-doped ZnO-SnO2 could be used as a significant material for selective detection of low-concentration ethanol gas [24]. Moghaddam et al. synthesized polythiophene nanocomposite nanofibers containing ZnO nanoparticles through a self-assembly process in the presence of CTAB as the surfactant. Gas-sensing tests showed that the chemiresistor based on the as-prepared hybrid has high sensitivity, excellent repeatability, long-term stability, and short response time to ammonia gas at room temperature. ZnO/PT hybrid-sensing mechanism to ammonia gas was presumed to be the effect of p-n heterojunction between ZnO nanoparticles and PT [25]. Nano-crystalline ZnO nanofiber mats were synthesized through combined sol-gel electrospinning techniques followed by calcination in which poly(styrene-co-acrylonitrile) and zinc acetate were used as the binder and precursor, respectively. The average diameter of the ZnO nanofibers decreased from 400 to 60 nm, while their grain size and crystallinity were enhanced by increasing the calcination temperature. Due to their high surface area and superhydrophilicity, these ZnO nanofiber mats were highly sensitive in sensing gaseous ammonia, and the sensitivity of these mats increased as a function of their calcination temperatures [26].
\nThe fabrication of CoO-ZnO composite nanofibers was achieved by electrospinning followed by calcination. In this 0.595 g of zinc acetate dihydrate and varied concentrations of cobalt oxide in the ratio of 1, 3, and 5 wt% were added to 10 ml of dimethylformamide solvent. All the reagents were purchased from Sigma-Aldrich Corporation. The solution was magnetically stirred for 5–6 hours at room temperature; subsequently 1 g of polyvinylpyrrolidone (PVP) and 8 ml of were added to the mixture and kept for further stirring of about 2–3 hours at room temperature. The resulting solutions were electrospun using commercial electrospinning apparatus (EC-DIG Electrospinning, IME Technologies). The composites were ejected from the needle of a syringe applying an electrical field, as high voltage of about 18 kV and at the needle with collector distance of 20 cm. Composite nanofibers were collected in the stationary mode on the surface of silicon substrates clamped on top of a conductive circular collector.
\nBy modifying the solvent and the electrospinning parameters, different tests were carried out to optimize the morphological properties of the synthesized composite nanofibers. While decomposition, we employed a negative and positive voltage between the needle and the substrates was 14–20 cm, and the flow of the solutions were set to 5 μl/min. The deposition times were kept constant at 3 min. The morphological characterization was performed by scanning electron microscopy (SEM) with a field emission gun. The atomic composition of the nanofibers was analyzed by energy-dispersive X-ray (EDX) spectroscopy using a solid-state detector. Gas-sensing performances are done at different temperatures like at room temp, 50°C, and 100°C to find out the optimum operating temperature for detecting acetone gas. The sensitivity studies of CoO-ZnO composite nanofiber were carried out over different concentrations of acetone gas from 50 to 250 ppm.
\nWhen Zn and O combine, Zn loses two valence electrons to O; thus, due to loss of an outer shell, the Zn atom shrinks in size, while O atom increases in size due to addition of an outer shell. The wide difference in size between zinc and oxygen atom will allow large space while providing foreign atoms to incorporate. Cobalt oxide-zinc oxide composite in the presence of ambient oxygen undergoes chemical absorptive changes by capturing electrons in conduction band which is known as recombination where oxygen molecule is adsorbed on its surface. This will decrease the barrier height for electrons to transport there by reducing the resistance value.
\nActivation energy is required for adsorption and desorption of oxygen and detection of gas. This in turn will control the recovery time and response as well. Temperature plays a major role in controlling this mechanism. The basic sensing mechanism in this thin-film sensors includes that at temperature which is less than or equal to 100°C where the adsorption mechanism takes place, that is, acetone gas passes over the sensing film oxygen molecules such as O2− will form and upon increase in temperature it will gets modified to 2O−and then O2−. Then in the presence of acetone gas, carbon dioxide gas is released and water and electrons are gained. In oxidizing mechanism, O2 atoms will be gained and in reduction mechanism atoms will be lost.
\nMostly semiconducting gas-detecting sensors, resistance will change according to the adsorption and desorption of gas molecules on the surface of the thin film [26, 27, 28, 29]. The ZnO nanofibers in the presence of air ambience will absorb the oxygen on the surface of the thin film. Later by collecting electrons from the conductance band, the oxygen adsorbed is changed into various chemical states. Thus, the nanofibers will show more resistance with the increment of the barrier height of e− to move. Since the target gas acetone is a reducing gas, the oxygen which is adsorbed on the surface will undergo oxidation with acetone. In this reaction resistance of sensor will reduce when e− enters the nanofibers as depicted in Figure 1.
\nSchematic sensing process of ZnO nanofibers.
The structure of the fibers plays a very vital role in getting sensitivity. Since the ZnO thin films will have high surface to volume ratio will have high sensing performance. Many earlier papers discussed about the ZnO thin film in sensing different gases. In this case the electrospinning technique used to synthesize nanofibers with various parameters will improve the sensor sensitivity for 5 wt% CoO-ZnO-calcinated nanofibers. Cobalt oxide is preferred in here to increase the catalytic conversion when compared with other dopants like Ni, Al, etc.
\nThe calcinated sample in the form of thin film is fabricated to form a handheld sensor for sensing the acetone gas. Here, the thin film is placed over a glass substrate and upon the masks of desire pattern is placed as shown in Figure 2. The physical vapor deposition technique, a thermal evaporation process, is followed for the fabrication of the thin films. The masks were prepared by using Co2 laser technique of comb pattern using acrylic sheet. Later the mask is placed on the thin film and these sensing materials are placed on the sample holder. Physical vapor deposition technique is followed for vaporization of the thin films where higher pressure is applied under higher temperature in a vacuum chamber. Indium electrodes were fixed by microsoldering technique, and copper leads were separated from the electrodes. The sensor is heated by placing upon a hot ceramic plate at 200°C.
\nSchematic of gas sensor.
Gas-sensing chamber has an inlet and an outlet chamber where the target gas is sent through an inlet nozzle. The sample is placed inside the chamber, and the two probes of electrometer were connected to the copper wires to measure the change in the resistance variation. One of the input parameters is the input voltage of about 3v; accordingly corresponding change in resistance and time variation is noted by injecting acetone of liquid form upon heating becomes gaseous state.
\nThe sensor resistances were calculated automatically by analysis system. The acetone liquid was injected in different concentrations of about 10, 50, 100, 150, 200, and 250 ppm. According to the volume of the chamber, this conversion is done, i.e., ml to ppm. This process is repeated at room temperature, 50°C, and 100°C by using ceramic heater to obtain the optimum temperature. The process is repeated until the gas evaporates properly from the gas chamber. The sensitivity is calculated by using the following formula:
\nRa—Resistance in air ambience.
\nRg—Resistance in the presence of target gas.
\nThe morphological, structural, and thermal analyses are discussed in this section.
\nTypical scanning electron microscopy (SEM) images of the synthesized nanofibers of pure ZnO nanofiber and cobalt-doped composite nanofibers of purity in the ratio of 1, 3, and 5 wt%, respectively, were shown in Figure 3. The nanofibers have a random orientation, as expected, due to the instability of the electrospinning jet. The EDX analysis (Figure 4) shows the presence of carbon, nitrogen, zinc, cobalt, and oxygen coming from PVP, zinc acetate, and cobalt oxide, respectively. The porosity of the electrospun mats is confirmed by detection of silicon signal coming from the substrate. The diameter of the nanofibers fabricated appears to be uniform, and it is also evident that it becomes thinner upon the increase of dopant concentration, i.e., 1 wt% CoO, 2 wt% CoO, and 5 wt% CoO, respectively. This is the result of addition of charges increased during the electrospinning process. Furthermore, a typical EDX spectrum of the Co-doped ZnO nanofibers was illustrated in Figure 4(d), which shows only the peaks associated with Zn, Co, and O atoms (Cu is from copper mesh grids) were detected. Therefore, the nanofibers were indeed Co-doped ZnO materials.
\n(a) Pure ZnO-calcinated fiber, (b) 1 wt% CoO-ZnO-calcinated nanofibers, (c) 3 wt% CoO-ZnO-uncalcinated nanofibers, and (d) 5 wt% calcinated CoO-ZnO composite nanofiber.
EDX analysis: (a) ZnO-calcinated fiber, (b) 1 wt% CoO-ZnO-calcinated nanofibers, (c) 3 wt% CoO-ZnO-calcinated nanofibers, and (d) 3 wt% CoO-ZnO-uncalcinated nanofibers.
Thermogravimetric (TG) analysis was performed on a NET-ZSCH STA 449 thermo-analyzer in an air atmosphere. The precursor fibers of PVP/zinc acetate/cobalt acetate composite produced after electrospinning must be annealed because of its high contents of organic polymer and ethanol solvents in these nanofibers. The thermal behavior of the precursor fibers of this composite is shown in Figure 5; it illustrates that most of the organics belonged to PVP and the CH3COO group and other volatiles (H2O, CO2, etc.) are detached at temperature < 600°C. Beyond 600°C, there is no change in weight loss, indicating the formation of pure inorganic oxide. The XRD curve depicted in Figure 6 shows all the peak positions of Co-doped ZnO nanofibers which suggested that Co is successfully incorporated into the crystal lattice of ZnO which is reasonable given that the ionic radii of tetrahedrally coordinated Co2+ and Zn2+ are similar.
\nTG curves of precursor fibers of PVP/zinc acetate/cobalt acetate composites.
XRD of Co-doped ZnO nanofibers after calcination at 600°C.
Optical properties of ZnO nanofibers and Co-doped ZnO nanofibers have been examined through Raman spectrometers and photoluminescence (PL). Figure 7 shows their typical PL spectra under an excitation wavelength of 325 nm; only a broad and strong ultraviolet (UV) luminescence with a maximum at 375 nm is observed. This ascribed to the near band edge emission of the wide band gap zinc oxide. Compared to the spectrum of ZnO nanofibers formed under the same experimental condition, the emission peak of Co-doped nanofibers is shifted about 10 nm toward longer wavelength significantly. ZnO powders are heavily doped by donors like In, and it was interpreted as mainly due to the sp-d exchange interactions between the band electrons and the localized d electrons of the Co2+ ions substituting Zn ions. The s-d and p-d exchange interactions lead to a negative and a positive correction to the conduction band and the valence band edges, resulting in a band gap narrowing.
\nPL structures of ZnO nanofibers (solid line) and Co-doped ZnO (dotted lines) after calcination at 600°C.
To confirm that the Co ions as the dopant were successfully incorporated into the crystal lattice of ZnO, Raman scattering spectra of ZnO nanofibers and Co-doped ZnO nanofibers were measured. As observed in Figure 8(a and b), the Raman spectrum of the ZnO nanofibers exhibits a strong peak at 437 cm−1, which has been assigned to the vibration mode E2 (H) characteristic of the ZnO with hexagonal structure. The peaks at 330 and 379 cm−1 are assigned to the vibration mode 2E2 and the A1 (TO) mode, respectively. In comparison to the Raman spectrum of ZnO nanofibers, most modes in Co-doped ZnO nanofibers figure, expecting the A1 (TO) mode is disappeared. The E2 (H) mode and the vibration mode 2E2 broaden asymmetrically and shift toward lower frequencies when the Co is doped. This is due to the broken symmetry induced by the incorporation of Co dopants into the ZnO structure and could be explained by the spatial-correlation model [30, 31]. Apart from these features, the Co-doped ZnO nanofibers exhibit an unusual peak at 540 cm−1, which has been assigned to the quasi-LO phonon mode (AM1) due to the abundant shallow donor defects, such as oxygen vacancies or zinc interstitials bounded on the tetrahedral Co sites [32]. These results together confirm that Co was successfully doped into the crystal lattice of ZnO nanofibers.
\nRaman spectra of (a) ZnO nanofibers and (b) Co-doped ZnO nanofibers after calcination at 600°C.
Gas-sensing performances were performed at different temperatures of ambient, 50°C, and 100°C to find out the optimum operating temperature for the detection of acetone gas. The sensitivity plots were measured for concentration of acetone gas against purity of the CoO-ZnO nanocomposite fibers fabricated. The sensitivity of the dopants was found to be increasing with increase in temperature and increases with the increase in dopant concentration when compared with the pure nanofibers.
\nWhen the operating temperature is low, the activation energy used will be reduced simultaneously, and in turn the response will be decreased. At the same time, if the operating temperature used is very high due to the increased activation energy, the adsorbed gas molecules will escape before the reaction occurs; thus, the response will decrease too. In this chapter, 5 wt% calcinated CoO-ZnO nanofibers at 100°C have high sensitivity when compared with other compositions like pure, 1, and 3 wt%. Hence this sample can be improvised to make use in many applications like diabetic’s detection where the ppm range should be between 1 and 3 ppm. The base resistance, i.e., in the presence of air ambience, will increase and in the presence of acetone gas will increase. Since the acetone gas will evaporate if exposed to air temperature, the gas injected should be properly according to time to time. The ppm is calculated according to the gas chamber volume. According to the volume of the gas chamber used in this chapter, the ppm is taken as 5 ml as 50 ppm. Acetone is calculated in terms of parts per million. The main feature of this chapter is to improvise the selectivity of acetone sensing with CoO-ZnO nanofibers.
\nFigure 9 shows the sensor sensitivity for pure ZnO nanofibers. In this plot the sensitivity will increase linearly from 50 to 250 ppm with increase in the acetone concentration. Such linear sensitivity will be helpful in sensing gases like acetone. When compared with that of the room temperature, pure ZnO nanofibers will show high sensitivity at 100°C. The plot is between concentrations in ppm vs. sensitivity.
\nSensitivity plot for pure ZnO nanofibers.
From Figure 10(a–g), the plots for 1, 3, and 5 wt% for both calcinated and uncalcinated, the sensitivity will linearly increase with increase in concentration and also increase if the dopant concentration increases along with the operating temperature increases, i.e., at the 100°C temperature.
\nSensitivity plots: (a) 1 wt% CoO-ZnO uncal, (b) 1 wt% CoO-ZnO cal, (c) 3 wt% CoO-ZnO uncalcinated, (d) 3 wt% CoO-ZnO cal, (e) 5 wt% CoO-ZnO uncalcinated, (f) 5 wt% CoO-ZnO calcinated, and (g) 5 wt% CoO-ZnO uncalcinated.
The above plot gives details about sensitivity plot for 5 wt% CoO-ZnO-uncalcinated nanofibers at concentration from 1 to 5 ppm where the linearity increment will be there from 3 to 5 ppm which is helpful in many applications in medical field like in diabetic’s detection.
\nSEM analysis is used to determine the composition, structure, and surface morphologies of fibers. The amount of acetone in ppm range is detected. The sensitivity, change in resistance, and variation of calcinated and uncalcinated fibers were analyzed. The analyses which were taken from these results, that is, change in calcinated nanofibers, show more response to acetone when compared with uncalcinated nanofibers. The reason behind this kind of statement is PVP decomposed on calcination and hence it shows more resistivity, whereas in the case of uncalcinated fibers, PVP will not allow the zinc oxide to respond. From the sensitivity plots, we note that sensitivity increases when the doping concentration increases but also increases in the case of calcinated fibers. This chapter will discuss the pure, 1, 3, and 5 wt% CoO-ZnO nanofibers for acetone detection. The results give information that 5 wt% CoO-ZnO nanofibers will improve the acetone-sensing performances efficiently even if the atmosphere is complicated. These results will be helpful in many acetone gas-sensing applications widely.
\nSituated at the heart of the African continent, in an arid Sahelian environment where every water point is a source of life, Lake Chad is at the heart of a complex issue related to the choice between economic activities and ecological balance. For a long time, priority was given to economic activities which, combined with the growing effects of global warming, have particularly led to the loss of 90% of its water surface area, thus going from 25,000 Km2 (in 1964) to less than 2,500 Km2 today and exposing the local resident populations that depend on it to climatic vulnerabilities [1]. NASA mapping surveys in 2001 (Figure 1-A below), corroborated by satellite imagery (Figure 1-B below), confirm this. These figures also allow us to note that the northern part of Lake Chad, integrating Niger and Nigeria, has completely disappeared, and is taken over either by vegetation or expanses of sand, or by human settlements.
\nSources: NASA 2001, Internet images. Notes: (A) Changes in the surface area of Lake Chad from 1963 to 2001. (B) Satellite images of Lake Chad in 1973, 1967, 1997 and 2001.
Today, safeguarding Lake Chad is a major concern for the planet. This concern is reflected in the willingness of the riparian states to restore this natural asset, through the construction of an inland waterway through which it would be supplied with water. In addition to its very high cost of construction and despite a high proportion of favorable opinions, this project is not socially desirable in the sense of Pareto since it could generate another cost for society. Indeed, the restoration of Lake Chad is likely to have contrasting effects not only on the environment, but also on the population engaged in agricultural activities on its shores and surroundings. In order to meet the Pareto criterion, these negative social consequences require compensatory transfers reflecting the loss incurred by the local population, mainly agricultural. How much should potential agricultural victims be compensated? What is the social benefit of such a project? These are two related questions that can be summarized in one main question, which is precisely the issue of this study: would the restoration of Lake Chad provide benefits that exceed the costs it would impose on farmers?
\nThe aim of this study is precisely to answer these questions by carrying out an economic analysis of this project in order to clarify and justify the appropriateness of its implementation. Specifically, the study aims to estimate and compare the benefits and costs that the implementation of the Lake Chad Restoration Project would entail, considering only the preferences and motivations of the populations of the Cameroonian part of Lake Chad, notably the Logone and Chari departments of the Far North Region of Cameroon. This choice was mainly justified by the unavailability of time and resources to carry out a regional study including the four countries bordering the natural area which are: Cameroon, Niger, Nigeria and Chad.
\nThe rest of this chapter is as follows: the next section sets out the analytical framework for the study. In sections 3 and 4, we carry out a monetary evaluation of the benefits and costs associated with the implementation of the project, specifying the methodological aspects specific to each part of the evaluation. Section 5 will allow us to compare these values and discusses the results obtained.
\nSafeguarding natural assets faces a complex dilemma to be resolved, because every choice, every option (to preserve the resource in its natural state, to let it degrade or to transform it for another use) has consequences in terms of gain or loss of value. Thus, it is only after carefully weighing all the values gained and lost under each used option that one can decide on the relevant option to be retained. For this, it is necessary to implement tools to facilitate the decision-making process. Among these tools, it is common to use cost–benefit analyses (CBA), which make it possible to shed economic light on the definition of a public objective, to make public decisions more transparent and to take into account the opinions of all the stakeholders concerned by these decisions.
\nIn our context, a CBA could be used to justify whether or not the project to restore Lake Chad should be carried out, as it allows a monetary comparison of the costs incurred and the resulting benefits for the population. Its theoretical foundations are essentially as follows: benefits and costs are defined respectively as gains and losses in human well-being or utility [2]. Thus, a project or policy satisfies the cost–benefit criterion if its social benefits are greater than the social costs it imposes on society1 as a whole [2, 3, 4].
\nHowever, in the case of environmental policies, the monetary evaluation of costs and benefits is difficult given the non-market nature of these goods and the services they provide. To overcome this issue, CBA assumes that individual preferences for environmental goods should be considered as the source of value. These are measured by a willingness to pay (WTP) in the case of a benefit and by a willingness to accept compensation (WTA) in the case of a cost [2]. In other words, an increase in an individual’s level of well-being can be measured by the maximum amount that the individual would be willing to pay to benefit from the policy. If, on the other hand, the policy results in a reduction in well-being, it will be measured by the amount of money that the individual in question would require as compensation for accepting the policy.
\nAmong the range of methods for valuing non-marketable environmental goods, it is common to use the contingent valuation method (CVM) to reveal these preferences [5, 6]. It is a method of valuing non-marketable goods that allows an estimate to be generated of the measures that compensate for the change in individual well-being induced by the implementation of a given public policy. Its objective is to create and simulate a hypothetical market, based on a questionnaire, in which agents are led to reveal their preferences in terms of WTP to receive a benefit and/or WTA as compensation for tolerating a given cost [7]. Using these values, it would now be possible to quantify the benefits or costs of the change one wants to make to the good concerned.
\nHowever, although CVM is the most widely used method for valuing environmental goods today [2, 5, 8], it has been strongly criticized2, calling into question the reliability and validity of the values obtained [9]. However, instead of stopping its use, these criticisms have instead made it possible for the work to perfect it both in terms of data collection techniques and the econometric treatment of responses [10]. Although still controversial, academics and policy makers increasingly recognize this approach as a flexible and powerful method for estimating WTP [2].
\nHowever, it is important to point out three orientations that set us apart from several studies using the methods presented above. Firstly, to our knowledge, with the exception of [11], most of the work on the use of CBA to evaluate policies for safeguarding wetlands has been mainly oriented towards a comparison between the benefits and investment costs, in order to determine the appropriateness of safeguarding them without seeing their potential productive use. Then, unlike [11]3, we are interested here in the benefits of the potential agricultural use of wetlands and use CBA to measure both the benefits of restoring Lake Chad and the cost involved. Finally, we disregard the principle of discounting, which makes it possible to know whether a project is profitable by comparing different values observed at different times. Indeed, the problem that concerns us here is the difference in time between economic and ecological investments. However, economic time has nothing in common with biological or ecological time, which can extend over several hundred years. Consequently, the updating process has a clear bias against the future for long-term environmental problems, as it implies a reduced interest in the future compared to the present, and is thus a deterrent to the protection of the natural environment. Moreover, the omission of such a principle does not pose a major problem insofar as its use in the evaluation of environmental projects is still debated.
\nThe monetary evaluation of the social benefits of the Lake Chad restoration project is based on two steps: the identification of the factors explaining the WTP of households wishing to participate in the Lake Chad restoration program and the calculation of the average WTP, which, when aggregated with the entire population concerned, enables us to obtain the said benefits.
\nBefore embarking on the analysis of these factors, we provide a brief overview of the methodology used.
\nFirst of all, we present the data used. Next, the econometric model used to identify the factors relevant to explaining household WTP is presented.
\nThe data used are from a contingent survey on individual preferences for saving Lake Chad that we conducted in 2011 among households in the Cameroonian part of Lake Chad. The sample is constructed through a combination of two reasoned choice survey methods, namely the quota method and the route method. The use of a probability survey requires a sampling frame (lists of households or dwellings), which unfortunately does not exist in the region, and it is impossible (limited time and means) to set it up. With this in mind, we set quotas for each itinerary and imposed on each respondent to follow a fixed itinerary (neighborhood), including stopping points for interviews. If an interviewer was absent or refused at a stopover point, the respondent moved on to the next point. This was one way to get around the problem of non-response generally observed in surveys. A total of 649 households were asked to reveal their WTP to finance the restoration of Lake Chad through face-to-face interviews conducted in their homes. Due to the elimination of questionnaires with incomplete or missing information, 623 questionnaires are retained for the study.
\nThe closed-ended question format with a simple dichotomous choice (yes/no) and the voluntary contribution were used in this survey as a means of formulating and revealing the WTP. The question asked, to which the respondent was asked to answer yes or no, is conveyed as follows: “Would you agree to pay €\n6\nCi per year, and for five years, to a special fund as a voluntary contribution, in the form of a donation, to ensure that Lake Chad is supplied with water? ”. The amount Ci offered to a respondent was chosen randomly from a vector of four bids (see Table 1 below), but with a concern that each amount be distributed equitably in the total sample.
\nProposed amount (€) | \n1.83 | \n3.66 | \n9.16 | \n18.32 | \nTotal | \n
---|---|---|---|---|---|
Enrollment | \n158 | \n155 | \n155 | \n155 | \n623 | \n
Proportion (%) | \n25.36 | \n24.88 | \n24.88 | \n24.88 | \n100 | \n
Distribution of proposed amounts in the sample.
In addition to their WTP to finance the restoration of Lake Chad that could be carried out there, the respondents revealed other information about themselves: socio-economic characteristics and opinions on the safeguarding of Lake Chad. Using an econometric model, this information will allow us to test the theoretical validity of the contingent study and to predict the average WTP of our sample associated with the offer to restore Lake Chad.
\nTables A1–A3 in Annex A, present descriptive statistics respectively on the socio-economic profile of households, the relationship between individuals and Lake Chad and the reasons for refusing to adhere to the contingent scenario.
\n\n
\nEmpirical specification of the econometric model\n
In our situation, the respondent agrees \n
With \n
\nData from the econometric analysis\n
The estimation procedure is performed on the data from the descriptive analysis above, from which false zeros were excluded as well as individuals who did not report their income or did not comment on the reasons for their refusal to pay the proposed amount. Thus, only information from 502 households was considered.
\nChoice of explanatory variables\n
In our study, we preferred to keep in the econometric analysis the variables most correlated with WTP. There are two main reasons for this choice. On the one hand, there is no consensus on the variables influencing the decision to pay or not to pay for wetland quality improvement and their signs. On the other hand, the elimination of irrelevant variables makes it possible to obtain a more efficient econometric model [13] and to reduce the problems of multicollinearity. Thus, out of thirteen potential variables represented by the individuals’ responses to the questionnaire, Chi-2 tests revealed six variables with a significant influence on WTP. The specification of these variables as well as the Chi-2 values and their associated significance thresholds are transcribed in the following Table 2.
\nVariables | \nSpecifications | \nChi-2 | \nProb. | \n
---|---|---|---|
\nDependent Variable\n | \n|||
REPONSE | \n= 1 if the respondent agrees to pay for the proposed offer and 0 if not. | \n\n | \n |
\nIndependent Variable\n | \n|||
Ln(OFFER) | \n= natural logarithm of the amount proposed in the referendum | \n14.776 | \n0.002 | \n
GENDER | \n= 1 if the respondent is a woman and 0 if not | \n8.758 | \n0.003 | \n
SIZE | \n= Number of individuals in the household | \n45.207 | \n0.048 | \n
INCOME | \n= 1 if the monthly household income is greater than €152.67 and 0 otherwise | \n3.563 | \n0.059 | \n
VISIT | \n= 1 If the respondent has already been to Lake Chad and 0 otherwise | \n32.849 | \n0.000 | \n
SENSITIVITY | \n= 1 if the respondent is sensitive to the preservation of natural assets and 0 if not. | \n72.588 | \n0.000 | \n
Definition of explanatory variables.
Contrary to our expectations, the variables: age, level of education, marital status, household area of residence, nationality, distance from Lake Chad and awareness of Lake Chad’s shrinkage lacked a significant relationship with WTP. However, we are somewhat surprised by the non-significance of the level of education, even though several French studies [13, 14] show that it has a considerable effect on WTP. Nevertheless, the results of studies conducted in developing countries are in our view, particularly those of [15] for Tunisia or [16] for China. This non-significance of the level of education can be explained by the fact that education is not the channel of transmission that enables the local resident populations to receive the knowledge required to appreciate the value of Lake Chad.
\nThe results of the Logit model with the significant variables are presented in Table 3, along with their marginal effects.
\nExplicative Variables | \nCoefficient | \nz-stat. | \nP > |z| | \nMarginal effects | \n
---|---|---|---|---|
Ln(OFFER) | \n- 0.506* | \n- 3.60 | \n0.000 | \n- 0.074 | \n
INCOME | \n0.581** | \n2.35 | \n0.019 | \n0.083 | \n
GENDER | \n- 0.542** | \n- 2.10 | \n0.035 | \n- 0.085 | \n
SIZE | \n0.087* | \n3.35 | \n0.001 | \n0.012 | \n
VISIT | \n0.752* | \n3.14 | \n0.002 | \n0.117 | \n
SENSITIVITY | \n1.755* | \n7.01 | \n0.000 | \n0.316 | \n
CONSTANT | \n3.134* | \n2.58 | \n0.010 | \n\n |
Pseudo R2\n | \n0.2212 | \nProb> chi-2 | \n0.0000 | \n|
Log of Likelihood | \n- 215.91359 | \nNumber of observations | \n502 | \n|
Correct predictions | \n82.47% | \n
Logistic regression results.
Notes: The model has been corrected for heteroskedasticity by White’s method. The signs * and ** indicate the significance of the coefficients at 1% and 5% respectively.
The results show that the model is significant (Prob> chi-2 = 0.0000) and the correct prediction rate is estimated to be 82.47%, suggesting that the model is generally well-specified. Since the estimated model has satisfactory explanatory power, let us now examine its results. With this in mind, we choose to divide the significant explanatory variables into three categories: economic, socio-demographic and behavioral.
\nIn the economic variable category, the referendum offer and household income have a significant effect on the probability that individuals reveal a positive willingness to pay. The amount offered has a negative effect, as expected, on this probability. Thus, the probability of answering positively to the WTP evaluation question decreases as the amount offered in the referendum increases. Several studies using the CVM with dichotomous choices confirm this finding, including [15, 16], who, in their respective studies, establish a negative relationship between the proposed offer and the probability of accepting to pay the offer.
\nContrary to the effect of the offer, household income positively influences the probability of agreeing to pay. As a result, households with higher incomes are also more willing to accept the amount offered than others. This result thus reflects the role that a stable and sufficient income could play in motivating individuals to participate in saving Lake Chad. This result is consistent with the finding of [15] but contradicts with [16], who instead found an insignificant relationship between income and the probability of agreeing to pay. Using a Tobit model, [14, 17] find results that are in our direction, notably a significantly positive relationship between income and respondents’ WTP.
\nThe signs of the economic variables somehow reassure the rationality of households in their response since they are in line with the expectations of economic theory [2]; indeed, the probability of accepting to pay decreases with the value of the proposed offer and increases with income. This finding allows us to theoretically validate our contingent study and to conclude that saving Lake Chad is not an inferior7 good for the local residents.
\nConcerning the socio-demographic profile of households, only two variables are significant: the gender of the respondent and the size of the household. The first has a negative effect on the probability of accepting the proposed amount. Based on this result, it can be assumed that women are less likely to pay than men. This may be explained by the fact that women do not have equivalent control over, or access to, household financial resources as men do. Thus, they are often reluctant or unable to commit the household to a substantial financial obligation. This finding is also observed by [14] who reveals that women have lower WTP than men. The second has a positive effect on the probability of accepting the bid. This result probably reflects the willingness of households to pass on this natural heritage to future generations. This result corroborates that of [15] regarding the positive significance of household size in explaining the probability of agreeing to pay for the proposed auction.
\nWith regard to the variables related to behavior in the face of Lake Chad, the results indicate a significant positive relationship between WTP and visiting Lake Chad. Individuals who have ever visited Lake Chad are certainly aware of the threat to it, and are therefore more willing to pay to preserve it than others. This finding is consistent with the work of [11], who finds a significant and positive relationship between the WTP revealed and visiting the Seine estuary. Similarly, environmental sensitivity has a positive effect on WTP, confirming the results of [14, 17]. As a result, individuals who feel concerned about the protection or safeguarding of natural environments tend to accept the bid offered more than others. These last two results highlight not only the positive role that environmental awareness and education can play in household policies to preserve Lake Chad, but also the value of media coverage of information on the negative effects of the degradation of this natural heritage.
\nThe main difficulty with logistic regression is that it does not allow direct measurement of average WTP. A procedure for calculating average WTP is therefore necessary.
\nThe calculation procedure we have adopted to estimate average WTP is based on Eq. 1 above and the truncation of the maximum supply from the questionnaire8. The expression for the corresponding average WTP9 is:
\nThe arguments of the other variables are not explicitly apparent10 in Eq. 2, but their influence is exerted on the values of the parameters \n
Average WTP is calculated based on the econometric results of the estimation of the previous Logit model (Table 3). Table 4 below gives the logarithmic value of mean WTP. Thus, the real mean WTP is obtained by applying the exponential function. The corresponding average WTP is estimated at €17 per household per year. This value is lower than those obtained in other studies carried out in developing countries and using CVM with dichotomous choices, such as those of [15, 16], who find an average WTP of €25 and $35 respectively. Also, it remains lower than French studies using payment cards: €25 for [14] or €45 for [11].
\nConstant (\nα\n) | \n3.134 | \n
---|---|
Coefficient of the offer (\nβ\n1) | \n- 0.506 | \n
Ln (Average WTP) | \n€0.014/year | \n
Average WTP | \n€17/year | \n
Calculation of average WTP.
Over five years, as planned in the contingent scenario, we end up with an average WTP of €85 per household to help finance the restoration of Lake Chad. This value, aggregated across all households that value Lake Chad, allows us to obtain the benefits associated with the project.
\nIn 2010, the population of this department was estimated at 551,718 individuals with an average of 7 people per household [18]. If we consider the national population growth rate (2.8%), we can estimate the population at 567,166 in 2011, which corresponds to an estimated 81,024 households. However, 502 households out of 623 value the preservation of Lake Chad, or 80.58% of the households in our sample.
\nThe social benefits related to the restoration of Lake Chad are obtained by multiplying the average WTP by 80.58% of households, for an estimated monetary value of €5,549,576.832. This value represents what people in the region would be willing to give to save Lake Chad.
\nThe objective here is to estimate the farmers’ willingness to receive financial compensation for the losses that would arise from the restoration of Lake Chad and to deduct the induced social cost. This section is thus structured around these two main axes.
\nIn order to measure the farmers’ willingness to receive financial compensation for the welfare losses that would result from the restoration of Lake Chad, we used the CVM.
\nWe successively present the data used, the econometric model used and the variables that could explain the farmers’ WTA.
\nData also come from the survey conducted in 2011 in the Cameroonian part of Lake Chad (presented above). This operation collected information on a sample of 98 farming households. The format of an open-ended question was used in this survey as a means of formulating and revealing the farmers’ WTA12 to bear the losses that would be related to the project’s implementation.
\nIn addition to their WTA, the interested parties revealed other information about themselves: socio-economic characteristics and opinions on the issues related to the safeguarding of Lake Chad. These indications, known as potential explanatory factors for the amounts expressed, are necessary for the econometric analysis of the latter, in order to predict an average WTA. By extrapolating the average WTA to the scale of the population concerned, this last calculation allows us to obtain the social costs resulting from the restoration of Lake Chad.
\nTables A4–A6 in Annex B present the socio-economic profile of the farm households in our sample and their attitudes towards the project, respectively.
\nThe objective of this model is to provide additional information that may help to better understand the formation of WTAs on household farmers. With this in mind, and depending on the ongoing nature of the WTA data, a simple regression model was chosen to identify the key explanatory factors for farmer WTA. The formulation of this model is as follows:
\nWhere \n
The specification of the variables that may explain the value of the WTA is given in Table 5 below.
\nVariables | \nSpecifications | \n
---|---|
STATUS | \n= 1 if the respondent is married and 0 if not | \n
AGE | \n= 1 if the individual’s age is between 30 and 60 years old and 0 if not | \n
Ln(INCOME) | \n= natural logarithm of annual household farm income | \n
SIZE | \n= Number of individuals in the household | \n
DISTANCE | \n= 1 if the distance between the home and Lake Chad is >20 Km and 0 if not | \n
FISHING | \n= 1 if the household participates in the fishing activity and 0 if not | \n
Definition of the explanatory variables of the WTA.
Estimation is performed on data from which false zeros have been excluded, including 7 farm households. In the end, information from 85 farm households is taken into account. To this information, we applied the simplest and most immediate method of estimation, namely ordinary least squares. Table 6 below presents the results obtained.
\nExplicative Variables | \nCoefficient | \nt-stat. | \nP > |t| | \n
---|---|---|---|
DISTANCE | \n- 0.176 NS | \n- 1.350 | \n0.181 | \n
AGE | \n0.067 NS | \n0.900 | \n0.369 | \n
STATUS | \n0.209* | \n1.860 | \n0.067 | \n
SIZE | \n0.013* | \n1.730 | \n0.088 | \n
FISHING | \n- 0.027 NS | \n- 0.280 | \n0.781 | \n
LN(INCOME) | \n0.684*** | \n10.600 | \n0.000 | \n
CONSTANT | \n3.677*** | \n4.210 | \n0.000 | \n
R2\n | \n0.7314 | \n\n | \n |
F-Stat. | \n29.84 | \n\n | \n |
Prob (F-Stat.) | \n0.0000 | \n\n | \n |
Number of observations | \n85 | \n
Results of the estimation of Eq. 3.
Notes: The model has been corrected for heteroskedasticity by White’s method. The ***, **, and * indicate the significance of the variables at thresholds of 1, 5, and 10 per cent, respectively. NS: Not significant.
The results presented in Table 6 above call for the following comments.
\nFirst of all, the WTA revealed by individuals is not a randomly announced value, but depends mainly on the level of farm income, probably reflecting the absence of strategic bias such as the desire to block the project or the announcement of compensation amounts that are totally disproportionate to the actual costs incurred by the project. The WTAs obtained should therefore be considered normal, as they are formulated in relation to the agricultural losses incurred. This result thus contradicts those often put forward in the literature, notably the abnormally high or infinite nature of the WTA values [20] or the popular conclusion that budgetary constraint does not play a role in the revelation of the WTA, but remains consistent with those of [21, 22, 23] who find a statistically significant and positive relationship between the levels of compensation requested by farmers and farm income.
\nSecond, the positive sign of the coefficient indicates that households with high farm incomes tend to demand higher levels of compensation than others. This result is somewhat reassuring to the rationality of the households in their response, and is indicative of the close relationship between farm income and the level of WTA. All factors being equals, the calculation of the income elasticity of the WTA shows that a 10% increase in income would result in a 6.84% increase in WTA, confirming the predominant role of agricultural income in the formulation of the WTA.
\nFinally, the results highlight the positive roles of marital status and household size on the WTA level. Couple households would thus require higher compensation than other households. Similarly, households with many members tend to express higher WTAs than others. These results can be partly explained by the strong tendency of these households to engage in agricultural activities [24], and therefore would be more vulnerable to the induced effects of the project. They also point to a greater loss of income for these farming households than would be induced by the project, and therefore suggest that any compensation should be modulated according to these criteria.
\nEconometric modeling provides, based on the estimated coefficients, the predicted WTA values for all individuals in the sample, even for those not included in the regression. The average WTA resulting from this modeling is thus estimated at €1,405.515 within a 95% confidence interval of [1,246.803; 1,564.226].
\nThe social costs associated with the Lake Chad restoration project are obtained by multiplying the average WTA by the number of potential victims. However, during data collection, it was not possible to determine the exact number of farming households that could be affected by the project. Therefore, this number was estimated based on the total agricultural population of the Cameroonian districts sharing Lake Chad, i.e., 27,00813 agricultural households in 2010.
\nAccording to the data collected, the social costs are estimated at €37,960,149.12 with a 95% confidence interval of [33,673,656.87; 42,246,614.57]. This value represents the monetary assessment of the damage that the Lake Chad restoration project would impose on the riparian agricultural population.
\nThe average WTP is estimated (in the medium term) to be €85 per household, or a total benefit of €5,549,576.832 from the Lake Chad restoration project. On the other hand, the average compensation to be paid to potential victims is in the order of €1,405.515, so the total cost of the project would be around €37,960,149.1214. It thus appears that the costs of restoring Lake Chad clearly outweigh the benefits. This result is in line with the conclusions of [25, 11] on the trade-off between productive/non-productive uses of wetlands, but contradicts the work of [22], which highlights the superiority of the benefits (12.6 million euros) over the costs (2.3 million euros) for the preservation of 2000 hectares (ha) of natural habitats along the Garonne River. This difference in results can nevertheless be explained by the observed differences in the sizes of the beneficiary populations (250,000 households) and their standards of living. For the average WTP is around €50.4/year and the average WTA around €1150/ha/year.
\nWe thus find ourselves in a paradoxical situation in which, in the medium term (5 years), the agricultural conversion benefits of Lake Chad are far greater than what the people of the region will be willing to give to save it. This result would therefore justify the trade-off to be made in favor of a productive use of Lake Chad’s wetlands. However, this difference must be tempered given:
The omission of negative externalities (water pollution, deforestation, etc.) in the calculation of the benefits induced15 by agricultural activities;
The overestimation of costs due to the impossibility of counting the potential victims of the project;
The low representativeness of the sample16 in relation to the total population living along the shores of Lake Chad;
The time horizon: economic or ecological. For an economic horizon (5 to 10 years), it is not profitable to carry out the project. On the other hand, if the ecological horizon is chosen (from 35 years), it becomes profitable to carry out the project, because the benefit/cost ratio would then become greater than 1, since the benefits of the project would then be around €38,543,518.56.
The amounts announced are therefore indicative for all policy choices, and must therefore be taken with caution. That said, the results obtained can nevertheless serve as a basis for deliberation among the parties concerned, without being a sufficient or necessary condition for the decision to implement a policy to restore Lake Chad. Other reasons may justify this policy.
\nAt the end of our study, it is very clear that the protection of Lake Chad is of crucial necessity, given the economic and environmental importance of this natural area. In economic terms, Lake Chad is much more than a source of recreation for its residents; it is the very foundation of their food security. It guarantees the economic and social dynamics of the region and constitutes, in a way, a pool of jobs linked to the economic activities that are found around it. From an environmental standpoint, the survey also reveals that Lake Chad is perceived by its neighbors and farmers as a true natural heritage and, as such, should be saved from its likely fate. To this effect:
73.51% of the riparian populations are in favor of the Lake Chad restoration project, and 63.40% would not hesitate to allocate a certain portion of their income to participate financially in the realization of such a project.
Despite their dependence on the productive services it provides, 70% of riparian farmers are in favor of the project. This percentage rises to about 87% when financial compensation is offered in return.
Its statistics are thus proof that, from the population’s point of view, there is a real benefit in preserving Lake Chad, even if the estimate of this benefit appeared low, or at least lower than that generated by the agricultural use of Lake Chad’s shores. However, this difference does not mean that the economy should be favored over the environment, but it does make it possible to highlight the possible perverse effects that could result from carrying out the Lake Chad restoration project without taking into account its potentially negative impact on agricultural activities, and thus on the food security of the riparian populations. In view of the above, we propose a new approach to save Lake Chad from its probable disappearance, one that takes into account both economic and environmental objectives. Thus, rather than opposing the quality of the environment to the pursuit of economic development, we believe that these two objectives are in close interaction and that a policy to save Lake Chad should ideally take into account all of them rather than focusing on one or the other. The economic literature speaks of sustainable development.
\nFurthermore, we believe that restoring Lake Chad without addressing the causes that led to its dwindling could be an unsustainable solution because the same causes will produce the same effects. Indeed, even if this natural asset were to be restored, there is no guarantee that it will not dwindle again and endanger the river that provides it.
\nThus, whatever the choice of decision-makers, it is essential to implement policies for the protection of this natural heritage. To this end, it is crucial to move from logic of supply management of the natural resources of the lake basin to logic of demand management of the said resources. This can be achieved by putting in place incentive mechanisms for the sustainable management of the wetlands of Lake Chad and the biodiversity associated with them.
\nSocio-Economic Profile | \nEnrolled | \nPercentage (%) | \n|
---|---|---|---|
SEX | \nMale | \n436 | \n69.98 | \n
Female | \n187 | \n30.02 | \n|
AGE | \nAverage of 30 years | \n187 | \n30.02 | \n
From 30 to 44 years | \n185 | \n29.70 | \n|
From 45 to 60 years | \n208 | \n33.39 | \n|
Above 60 years | \n43 | \n6.90 | \n|
MARRITAL STATUS | \nMarried | \n321 | \n51.52 | \n
Divorced / Widow/Widower | \n118 | \n18.94 | \n|
Single | \n184 | \n29.53 | \n|
LEVEL OF EDUCATION | \nNone | \n151 | \n24.24 | \n
Primary | \n77 | \n12.36 | \n|
Secondary 1st cycle | \n74 | \n11.88 | \n|
\n | \nSecondary 2nd cycle | \n177 | \n28.41 | \n
Tertiary | \n102 | \n16.37 | \n|
Not stated | \n42 | \n6.74 | \n|
AVERAGE MONTHLY INCOME | \nLess than €30.53 | \n75 | \n12.04 | \n
From €30.54 to €76.34 | \n147 | \n23.60 | \n|
From €76.35 to €152.67 | \n116 | \n18.62 | \n|
From €152.68 to €305.34 | \n97 | \n15.57 | \n|
From €305.35 to €763.36 | \n99 | \n15.89 | \n|
Above €763.36 | \n59 | \n9.47 | \n|
Is not pronounced | \n30 | \n4.82 | \n
Sample distribution by socio-economic profile.
\n | Number of observations | \nPercentage (%) | \n
---|---|---|
Sensitivity to the protection of natural environments | \n414 | \n66.45 | \n
Visits to Lake Chad | \n382 | \n61.32 | \n
Awareness of Lake Chad dwindling | \n499 | \n80.10 | \n
Favorable to the Lake Chad Restoration Project | \n458 | \n73.51 | \n
Agrees to pay the proposed amount | \n395 | \n63.40 | \n
Relationship and preferences of individuals towards Lake Chad.
\nReasons\n | \nType of zero | \nEnrolled | \nProportion | \n
---|---|---|---|
It’s not up to you to pay | \nFalse zero | \n51 | \n22.37% | \n
You do not consider this action necessary | \nTrue zero | \n66 | \n28.95% | \n
Your financial means do not allow you to do so | \nTrue zero | \n51 | \n22.37% | \n
You do not have enough information to decide | \nFalse zero | \n25 | \n10.96% | \n
You are afraid to pay for others | \nFalse zero | \n13 | \n5.70% | \n
Other reasons | \nFalse zero | \n14 | \n6.14% | \n
No opinion | \nFalse zero | \n8 | \n3.51% | \n
Total | \n228 | \n100% | \n
Reasons for refusing to pay the proposed amount.
Annex B. Descriptive statistics of the Farmers\' WTA Revelation Scenario
\nSocio-Economic Profile | \nEnrolled | \nProportion | \n|
---|---|---|---|
Average size of household | \n9.74 | \n\n | |
Civil status | \nMarried | \n77 | \n78.57% | \n
Divorced | \n13 | \n13.27% | \n|
Single | \n8 | \n8.16% | \n|
Sex | \nMale | \n86 | \n87.76% | \n
Female | \n12 | \n12.24% | \n|
Age | \nAge < 30 | \n19 | \n19.39% | \n
30 ≤ Age < 45 | \n33 | \n33.67% | \n|
45 ≤ Age ≤ 6 | \n25 | \n25.51% | \n|
Age > 6 | \n21 | \n21.43% | \n|
Level of education | \nNone | \n53 | \n54.08% | \n
Primary | \n32 | \n32.65% | \n|
Secondary 1er cycle | \n9 | \n9.18% | \n|
Secondary 2nd cycle | \n4 | \n4.08% | \n
\nSurface area in hectares (ha)\n | \nEnrolled | \nProportion | \nAverage farm income (€) | \n
---|---|---|---|
\nSurface area\n < 1 | \n4 | \n4.08% | \n885.50 | \n
1 ≤ \nSurface area\n < 2 | \n15 | \n15.31% | \n1,548.09 | \n
2 ≤ \nSurface area\n < 5 | \n18 | \n18.37% | \n2,256.49 | \n
5 ≤ \nSurface area\n < 10 | \n32 | \n32.65% | \n2,297.71 | \n
\nSurface area\n ≥ 10 | \n29 | \n29.59% | \n3,734.35 | \n
TOTAL | \n98 | \n100% | \n2,144.43 | \n
\n | Enrolled | \nProportion | \n|
---|---|---|---|
Opinion of the project | \nVery important | \n15 | \n15.31% | \n
Important | \n54 | \n55.10% | \n|
Less important | \n26 | \n26.53% | \n|
Not important at all | \n03 | \n3.06% | \n|
Favorable for compensation | \nYes | \n85 | \n86.73% | \n
No | \n13 | \n13.27% | \n|
Reasons for Refusal | \nType of zero | \n\n | \n |
I do not feel concerned | \nTrue zero | \n0 6 | \n46.15% | \n
I want to continue my activities | \nFalse zero | \n05 | \n38.46% | \n
This action is not necessary | \nFalse zero | \n02 | \n15.38% | \n
Assume that the individual\'s utility depends on improvement in the quality of a good\'s environment q, its income R and other observable socio-demographic variables m. Based on these hypotheses, we retain the linear utility function as follows:
\nIn a closed question, the respondent agrees to pay an amount of money C in order to benefit from the restoration of Lake Chad, if
\nThe induced variation in utility \n
Let F be the distribution function of a logistic law, then the probability \n
From this equation (4), the average WTP is defined by:
\nIn order to calculate this integral, it is necessary to look for a primitive of the function which is inside the integral.
\nFirst of all, let\'s assume:
\nNext, let\'s look for:
\nIn this perspective, we will multiply the numerator and denominator of the function within the integral by \n
Where:
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\\n\\nThe same principles apply to Works published under the CC BY-NC-SA 3.0 license, with the caveats that (1) the content may not be used for commercial purposes, and (2) derivative works building on this content must be distributed under the same license. The restrictions contained in these license terms may, however, be waived by the copyright holder(s). Users wishing to circumvent any of the license terms are required to obtain explicit permission to do so from the copyright holder(s).
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\\n\\nAll rights to Books and all other compilations published on the IntechOpen platform and in print are reserved by IntechOpen.
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\\n"}]'},components:[{type:"htmlEditorComponent",content:'Copyright is the term used to describe the rights related to the publication and distribution of original Works. Most importantly from a publisher's perspective, copyright governs how Authors, publishers and the general public can use, publish, and distribute publications.
\n\nIntechOpen only publishes manuscripts for which it has publishing rights. This is governed by a publication agreement between the Author and IntechOpen. This agreement is accepted by the Author when the manuscript is submitted and deals with both the rights of the publisher and Author, as well as any obligations concerning a particular manuscript. However, in accepting this agreement, Authors continue to retain significant rights to use and share their publications.
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LICENSE | \n\t\t\tUSED FROM - | \n\t\t\tUP TO - | \n\t\t
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The CC BY 3.0 license permits Works to be freely shared in any medium or format, as well as the reuse and adaptation of the original contents of Works (e.g. figures and tables created by the Authors), as long as the source Work is cited and its Authors are acknowledged in the following manner:
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\n\nThe same principles apply to Works published under the CC BY-NC-SA 3.0 license, with the caveats that (1) the content may not be used for commercial purposes, and (2) derivative works building on this content must be distributed under the same license. The restrictions contained in these license terms may, however, be waived by the copyright holder(s). Users wishing to circumvent any of the license terms are required to obtain explicit permission to do so from the copyright holder(s).
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\n\nAll rights to Books and all other compilations published on the IntechOpen platform and in print are reserved by IntechOpen.
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\n\nAll Long Form Monographs/Compacts are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license granted to all others.
\n\nCopyright to the individual Works (Chapters) belongs to their specific Authors, subject to an agreement with IntechOpen and the Creative Common license granted to all others to:
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\n\nAll Video Lectures under IntechOpen's production are subject to copyright and are property of IntechOpen, unless defined otherwise, and are licensed under the Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license. This grants all others the right to:
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