Environ Sci Pollut Res DOI 10.1007/s11356-015-4110-x
RESEARCH ARTICLE
Exploring factors influencing farmers’ willingness to pay (WTP) for a planned adaptation programme to address climatic issues in agricultural sectors Adeel Ahmed & Muhammad Mehedi Masud & Abul Quasem Al-Amin & Siti Rohani Binti Yahaya & Mahfuzur Rahman & Rulia Akhtar
Received: 12 September 2014 / Accepted: 11 January 2015 # Springer-Verlag Berlin Heidelberg 2015
Abstract This study empirically estimates farmers’ willingness to pay (WTP) for a planned adaptation programme for addressing climate issues in Pakistan’s agricultural sectors. The contingent valuation method (CVM) was employed to determine a monetary valuation of farmers’ preferences for a planned adaptation programme by ascertaining the value attached to address climatic issues. The survey was conducted by distributing structured questionnaires among Pakistani farmers. The study found that 67 % of respondents were willing to pay for a planned adaptation programme. However, several socioeconomic and motivational factors exert greater influence on their willingness to pay (WTP). This paper specifies the steps needed for all institutional bodies to better address issues in climate change. The outcomes of this paper will support attempts by policy makers to design an efficient Responsible editor: Philippe Garrigues A. Ahmed (*) : M. M. Masud Faculty of Economics and Administration, University of Malaya, 50603 Kuala Lumpur, Malaysia e-mail: [email protected] A. Q. Al-Amin International Business School (IBS), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia S. R. B. Yahaya The Centre for Poverty and Development Studies, Faculty of Economics and Administration, University of Malaya, 50603 Kuala Lumpur, Malaysia M. Rahman Faculty of Business and Accountancy, University of Malaya, 50603 Kuala Lumpur, Malaysia R. Akhtar Faculty of Economics and Management Sciences, International Islamic University, Kuala Lumpur, Malaysia
adaptation framework for mitigating and adapting to the adverse impacts of climate change. Keywords Adaptation . Agriculture . Climate change . Willingness to pay (WTP) . And contingent valuation method (CVM)
Introduction Climate change is becoming an ever more important issue in our lives. Presently, it is the leading global environmental problem. Extreme weather conditions, unexpected temperature and rainfall fluctuations are among the serious consequences of climate change and present a significant risk, particularly to agro-based economies (Tubiello et al. 2007; Georgescu et al. 2011). Most developing countries have agrarian economies that depend heavily upon agriculture, which in turn is the most vulnerable sector to climate changes (IPCC 2011). The climatic factors that influence agricultural productivity include rainfall patterns, temperature hikes, changes in sowing and harvesting dates, water availability, evapotranspiration and land suitability. There are serious concerns for the sustainable productivity of agricultural production in South Asia curtsy of abiotic and biotic stress. An abiotic climate change is a major factor threatening food security of the burgeoning population of South Asia, particularly Pakistan (Sultana et al. 2009). Similar findings were reported in recent IPCC publications and reports (IPCC 2007). Despite having climate diversifications in Pakistan, wheat is grown almost all over Pakistan and constitutes the staple food crop. This includes the arid regions of central and southern parts of Pakistan which are also considered food granaries (Klein et al. 2006). Since wheat is the staple food crop in
Environ Sci Pollut Res
Pakistan, the estimated land on which it is cultivated is 8693 ha, with a per hectare wheat yield of 2787 kg/ha. Per head wheat consumption in Pakistan is 124 kg/year where wheat flour supplies some 72 % of daily caloric intake per capita which is the largest in the world. These characteristics peculiar to wheat distinguish it from other food crops in Pakistan (GoP 2013). Figure 1 shows Pakistani wheat production in the last 5 years. Over the past few decades, wheat productivity increased extravagantly due to green revaluation. However, due to recent climatic variations, Pakistan’s arid zones are facing severe water stress, which has significantly damaged human life and economic activities (particularly agriculture). Pakistan has been experiencing growing temperatures resulting in extreme weather and climate variability. In view of agriculture’s dependence on climate factors (light, water and heat), crop yield will be significantly affected by climatic endorsed changes (World Meteorological Organization 1979). Wheat yield is also very sensitive to temperature increase. If the average temperature increases by 2.5 °C, wheat yield will reduce by 60 % (EIU, Pakistan Country Report 1994). The decrease in crop production in some regions is curtsy of prolonged dry spells and heat waves through moisture and thermal stress (Rounsevell et al. 2006). It was proposed that wheat yields will face serious climatic stress in the South Asian region. According to the fourth IPCC report, cereal yield in South Asia could decrease up to 30 % by 2050 coupled with a decline of gross per capita water availability from 1820 m3 in 2001 to 1140 m3 in 2050. In the case of Pakistan, water supply is scarce in many parts of the country. In the near future, a dramatic decline in water availability would result in a sharp decline in agricultural productivity. Thus, there is an urgent need to understand the impact of possible climate change in order to minimise such potential effects in addition to preparing for such an outcome (Sultana et al. 2009). Although climate change poses a serious threat to livelihoods and the economy, many farmers appear to have little Fig. 1 Wheat production of Pakistan Source: Pakistan Bureau of Statistics
concern for it. This is probably due to ignorance of the severity of the issue, weak institutional capacity, limited environmental and adaptation issues and lack of local knowledge (Howden et al. 2007; Lindner et al. 2010; Kurukulasuriya and Rosenthal 2013). The significance of climate in wheat production demands a comprehensive and efficient adaption framework (which can help to adapt effects of climate change) to determine farmers’ perceptions and attitudes toward changing climate. Accordingly, to ascertain an efficient adaptation framework, there is a need to analyse the applicability of climate change adaptation policies. Doubtless, climate change directly and indirectly affects the social and economic sustainability of farmers. It can cause crop failures and lead to heightened costs of production thereby reducing incomes by reducing productivity and ultimately raising the seasonal unemployment rate (Siwar et al. 2009; Alam et al. 2011). Since farmers largely depend upon agriculture, when agricultural production decreases, with it their revenues. This reinforces the need of adaptation strategies for farmers because such failures may cause chronic losses even leading to social anarchy (Challinor et al. 2010). According to Stern (Stern 2006), we are in the eleventh hour and must take serious steps to address the looming risk of climate change on society. We require adaptation strategies with planned implementation programmes. Their structure must go beyond encountering the adverse effects of climate change and should encircle a large number of social, economic and technical environmental challenges (Weitzman 2009; Shove 2010). Any adaptation method must jointly consider socioeconomic and climate change. Practitioners need to focus on the relevancy of future climate change and future society rather than social norms. The formidable challenge for policy makers is to identify relevant adaptation strategies that are susceptible to the population. At the same time, they face the dilemma that the adaptation strategies to climate change cannot provide equal benefits to all regions and ethnic groups. However, to reduce short and long-term vulnerabilities of climate change, adaptation is imperative (IPCC 2007).
Environ Sci Pollut Res
Since Pakistan has already experienced significant changes in weather in the form of floods, droughts, heat waves and tropical cyclones (Dai et al. 2004; Trenberth 2011), to tackle the challenges posed by climate change, knowledge of farmer perceptions toward climate change and their choice of adaptation method, in addition to the barriers affecting their adaptation of climate change is mandatory. Adaptation to climate change is a vital instrument that can reduce the adverse effects of climate change and has the potential to promote sustainable development through enhancing the welfare of the poorest members of society (Moss et al. 2010). Through adaptation, we can reduce vulnerability, increase resilience, moderate the risk of climate impact on lives and livelihoods and take advantage of opportunities posed by actual or expected climate change. Against the above background, the major objective of the study is to explore farmer perceptions and adaptation strategies to climate change, particularly of those growing wheat in Pakistan. The specific objectives of the study are: 1. To estimate the willingness to pay (WTP) of farmers for a planned adaptation programme of climate change in Rahim Yar Khan Punjab, Pakistan (Cannel irrigated area with the highest wheat production). 2. To determine the factor influencing their WTP. The study of WTP of farmers is important especially given the fact that no such research thus far been conducted in Pakistan. Therefore, there is a pressing need to survey the behaviour and WTP of Pakistani farmers for a planned adaptation programme to climate change in Rahim Yar Khan District of Punjab, Pakistan. This study will assist in the development of a suitable alternative framework for combating the adverse effects of environmental change and in the articulation of adaptation policies. In order to develop a sound understanding of WTP for the proposed climate change adaptation programme and socioeconomic and motivation factors that influence farmers’ WTP, we test the following two hypotheses. H1: Farmers’ socioeconomic factors have a positive direct relation with their willingness to pay (WTP) for a planned climate adaptation programmes. H2: Motivation factors have a positive and significant relation with WTP for planned climate adaptation programmes. Contingent valuation method A contingent valuation (CV) method was employed to estimate the WTP of farmers as applied to environmental valuation (Carson 2012). Many studies employed contingent valuation method (CVM) to quantify the benefits of non-marketed environmental goods and attributes in such a way they can enter directly into cost benefit calculations predominantly in developed Western, North American and East Asian countries
(Carson et al. 2010). The CVM methodology highlights diverse issues like improvements in water quality and sanitation (Howard et al. 2010; Vörösmarty et al. 2010; Orgill et al. 2013), valuing forestry (Canadell and Raupach 2008; Gelo and Koch 2012; Mason et al. 2013), exposure to flood risk (Lantz, et al. 2012; Kellens et al. 2013), wetland conservation (Yoon 2009; Kaffashi et al. 2013; Turner 2013), offsetting carbon emissions and groundwater contamination, health economics (Georgiou and Turner 2012; Del Borghi et al. 2013; Everard et al. 2013; Andersson, et al. 2014), cultural economics (Carvalho et al. 2010; Wicker et al. 2012), transportation safety and economics (Hess et al. 2012; Kristiansen 2013) and a wide range of environmental services (Ojeda et al. 2008; Vo et al. 2012). Furthermore, studies regarding WTP are conducted in developed countries and mainly examine consumers’ WTP for renewable energy concentrating on environmental (Bain et al. 2012; Park, et al. 2013) and health and social effects (Curtis 2012; de Bekker‐Grob et al. 2012; Damigos et al. 2009; Bollen et al. 2010; Sovacool 2011). However, most of these studies used CVM and mainly relied on choice experiments in recent times. Despite the diversity of study designs, all of them unanimously affirm that consumers generally have a positive attitude in terms of WTP. In this paper, our goal is to elicit what farmers in Rahim Yar Khan Punjab, Pakistan, are willing to pay for a planned adaptation programme to climate change. Survey questionnaires were used to ascertain the “willingness to pay” or “willingness to accept” (respectively), which means that the accuracy of contingent valuation methods depends on the quality of the survey instrument and how well people responded to the required assessments. The information component of the survey instrument or the CV scenario description of the item that is to be valued, the explanation of the method of provision, payment vehicle, the decision rule, and the period of payment (Boyle 2003) were presented to the farmers through a payment card. In this study, we developed a payment card according to Rowe et al. (1996). A payment card lists a series of values from which respondents choose an amount that best represents their maximum WTP. Payment card was designed with an exponential response scale that contains 24 cells. An exponential response scale is consistent with this hypothesis of measurement error increasing with WTP values. The values of the cells 2 through to 22 are computed (Table 1) by the following equation: Bn ¼ B1 ð1 þ K Þn−1
where, Bn =bid value, B1 =1, K=range selected for the payment card so that the largest value on the payment card is (1.2860)21 =200.
Environ Sci Pollut Res Table 1
Payment card
PKR0 ($ 0) PKR0.50 ($0.005) PKR1 ($0.010)*
Range PKR0–PKR 200 ($0–2.04)
Centre PKR 16 ($0.16)
PKR 2 ($0.020) PKR 3 ($0.030) PKR 4 ($0.041)
PKR 10 ($0.10) PKR 12 ($0.12) PKR 16 ($0.16)
PKR490 ($0.051) PKR 6 ($0.061) PKR 8 ($0.081)
PKR 20 ($0.20) PKR 25 ($0.26) PKR 35 ($0.36)
Function 1.2860 (n−1) PKR 45 ($0.46) PKR 55 ($0.56) PKR 75 ($0.77)
PKR 95 ($0.97) PKR 120 ($1.22) PKR 155 ($1.58)
PKR 200 ($2.04) ≥PKR 200 ($2.04) Don’t know
*$1=PKR 98
The payment vehicle was the electricity bill of the respondents. The fund will be managed by the climate change adaptation fund for agriculture, a non-government organisation (NGO) established specifically for that purpose.
Methodology Site selection and data collection The questionnaire used in this study was based on a survey among farmers of the canal-irrigated area of Rahim Yar Khan Punjab, Pakistan, in the southern part of Rahim Yar Khan as shown in Fig. 2. It covers approximately 1,188,000 ha, of which 795,000 ha is irrigated area. Approximately 254, 000 ha is allocated for wheat, 89,000 ha for sugarcane and 24,030 ha for wheat production.
Fig. 2 Map of Rahim Yar Khan Punjab, Pakistan
Survey design and sampling methods The study was conducted through direct face-to-face interviews in order to obtain reliable responses from the respondents. The study area is located in the southern part of Rahim Yar Khan which contains five wheat cultivated localities viz.: (1) Bhong, (2) Hamidabad, (3) Machhka, (4) Rahim Yar Khan, and (5) TarindaSawae-Khan of district Rahim Yar Khan all within a radius of 10 km of Rahim Yar Khan City were surveyed between January to February 2002. Of the eight areas and using a random sampling method, 80 farmers were selected with a total sample size of 400 (80*5). The survey was conducted in September, 2013. The survey was confined to within Rahim Yar Khan Punjab, as it constitutes a prominent agricultural zone in Pakistan. The data was collected through interviews with heads of households who worked as wheat farmers.
Environ Sci Pollut Res
Design of the questionnaire The questionnaire consisted of sections A, B and C. Section A collected information on the farmers’ socioeconomic characteristics (i.e. household size, gender, ethnic group, religion, education level, income, type of farmers, farm size and family size of the respondents). Section B enquired as to farmers’ perceptions and attitudes toward climate change. Section C consisted of CVM questions to estimate farmers’ WTP for a planned adaptation programme to climate change. This section also included some motivational factors that motivate them for WTP for climate change adaptation. In this section, farmers were asked to rank the motivation factors, which they feel are very important to motivate them for WTP for climate change adaptation. The most important item was to be ranked 1, the second most important 2, and so on. All items had to be ranked and no rank could be used more than once. Having collected the data from 283 farmers, we ranked all the motivating factors based on mean values. The lowest mean value was assigned the rank 1, which indicates the most important factor. Methods of data analysis The statistical analysis was carried out using SPSS. The analysis of this paper starts with respondents’ demographic profile followed by descriptive statistics of socioeconomic characteristics. A statistical analysis was employed to analyse the socioeconomics and motivation factors which influence farmer’s WTP for climate change adaptation. Chi-square tests also were administered to explore significant relationships between the amount of WTP bids and the factors used in the model. The χ2 value was employed to evaluate the influence of each factor on WTP with 0.05 used as the level of significance.
Specification of the research model A regression model was developed to explore the factors that might affect the WTP of farmers for a planned adaptation programme to climate in Rahim Yar Khan Punjab, Pakistan. Respondents were offered a yes/no option to select their WTP for a planned adaptation programme to climate change. When the dependent variable is in the 0–1 style, researchers can choose between logistic regression and probit regression (Wang et al. 2007). For this reason, in this study, logistic regression was selected as the evaluation method. It was assumed that the factors listed in Table 6 might affect WTP and these factors were included in the model as independent
variables. The probability model of the WTP is P(Yi =1), which was represented as: The basic model of the logit estimation is as follows: 3 2 h π i ; ……X p 7 6 PðY ¼ 1ÞjX 1 Loge 4 ¼ Log 5 e 1−π 1−P Y ¼ 1X 1 ; ……:X p ¼ α þ β1 X 1 þ ……… þ β p X p ¼ α þ
p X
β jX j
j¼1
π is a conditional probability of the form P(Y=1|X1,…,Xp). That is, it is assumed that success is more or less likely depending on a combination of values of the predictor variables. The log odd, as defined above, is also known as the logit transformation of π and the analytical approach described here is sometimes known as logit analysis. The logistic function takes the form of: Xp αþ β Xj D E j¼1 j e P Y ¼ 1X 1 ; ……X p ¼ Xp 1þe
αþ
j¼1
β jX j
This can also be transformed into: D E 1 P Y ¼ 1X 1 ; ……X p ¼ Xp 1þe
−α−
j¼1
β jX j
The non-response probability is P Y ¼ 0jX 1 ::::::X p ¼ 1−p Y ¼ 1jX 1 ::::::X p
1
¼
p
1þe
aþ∑ j¼1 β j X j
‘Yes’ (=1) if the farmers state a positive WTP and ‘No’ (=0) when they are not WTP any amount. The independent variables employed to predict the probability of WTP is age, education, household income, farm size and motivational factors such as ‘the environment has the right to be protected irrespective of the costs’, ‘I care about the environment in general’, ‘I feel responsible for my contribution to climate change’, ‘impacts on agricultural production’ and ‘concern for the risk posed by climate change’. Using the set of predictors, the LR equation for the log odds in favour of WTP is estimated to be: p log ¼ bo þ bi þ x i 1−p
Environ Sci Pollut Res Table 2
Variables included in the logit models
Variables
Description of the variables Category
Dependent variable: WTP
WTP for climate change adaptation
Independent variables Education Household size Farm size Income
The environment has the right to be protected irrespective of the costs I care about the environment in general I feel responsible for my contribution to climate change Impacts on agricultural production Concern for the risk posed by climate change
1=Willing to pay 0=Not willing to pay
Educational status
1=No formal education, 2=Primary, 3=Secondary, 4=Tertiary, 5=University Number of household members Area of farm Monthly family income 1=PKR 2000 ($20) and less, 2=PKR 2001 ($20)–4000 ($40), 3=PKR 4001 ($40)-6000 ($61), 4=PKR 6001 ($61)–8000 ($82), 5=PKR 8001 ($82)–10,000 ($102) and above PKR 10,000 ($102) Motivation factors for WTP 1=Strongly Disagree, 2=Disagree, 3=Neutral, 4=Agree and 5=Strongly Agree
1=Strongly Disagree, 2=Disagree, 3=Neutral, 4=Agree and 5=Strongly Agree 1=Strongly Disagree, 2=Disagree, 3=Neutral, 4=Agree and 5=Strongly Agree 1=Strongly Disagree, 2=Disagree, 3=Neutral, 4=Agree and 5=Strongly Agree 1=Strongly Disagree, 2=Disagree, 3=Neutral, 4=Agree and 5=Strongly Agree
Using the partial coefficients, bi, informing the change to log odds of agreeing to pay for a planned adaptation programme to climate change in the canal-irrigated area of Rahim Yar Khan Punjab, Pakistan. The independent and dependent variables used in the logit analysis and their basic statistics are
given in Table 2. A statistical analysis was employed to analyse the socioeconomics and motivation factors which influence farmer’s WTP for climate change adaptation.
Results and discussions Table 3
Socioeconomic characteristics of the respondents (n=385)
Demographics
Frequency
Percentage
Age 18–30 years
32
7.02
170 176 22
50.38 38.96 3.64
56 150 104 82 8
11.95 18.96 44.94 21.30 2.85
20 140 110 75 25 30 400
7.27 33.77 29.87 20.78 5.71 2.60 100
31–45 years 46–60 years 60 years and above Education level No formal education Primary Secondary Tertiary University Income
RESEARCH ARTICLE
Exploring factors influencing farmers’ willingness to pay (WTP) for a planned adaptation programme to address climatic issues in agricultural sectors Adeel Ahmed & Muhammad Mehedi Masud & Abul Quasem Al-Amin & Siti Rohani Binti Yahaya & Mahfuzur Rahman & Rulia Akhtar
Received: 12 September 2014 / Accepted: 11 January 2015 # Springer-Verlag Berlin Heidelberg 2015
Abstract This study empirically estimates farmers’ willingness to pay (WTP) for a planned adaptation programme for addressing climate issues in Pakistan’s agricultural sectors. The contingent valuation method (CVM) was employed to determine a monetary valuation of farmers’ preferences for a planned adaptation programme by ascertaining the value attached to address climatic issues. The survey was conducted by distributing structured questionnaires among Pakistani farmers. The study found that 67 % of respondents were willing to pay for a planned adaptation programme. However, several socioeconomic and motivational factors exert greater influence on their willingness to pay (WTP). This paper specifies the steps needed for all institutional bodies to better address issues in climate change. The outcomes of this paper will support attempts by policy makers to design an efficient Responsible editor: Philippe Garrigues A. Ahmed (*) : M. M. Masud Faculty of Economics and Administration, University of Malaya, 50603 Kuala Lumpur, Malaysia e-mail: [email protected] A. Q. Al-Amin International Business School (IBS), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia S. R. B. Yahaya The Centre for Poverty and Development Studies, Faculty of Economics and Administration, University of Malaya, 50603 Kuala Lumpur, Malaysia M. Rahman Faculty of Business and Accountancy, University of Malaya, 50603 Kuala Lumpur, Malaysia R. Akhtar Faculty of Economics and Management Sciences, International Islamic University, Kuala Lumpur, Malaysia
adaptation framework for mitigating and adapting to the adverse impacts of climate change. Keywords Adaptation . Agriculture . Climate change . Willingness to pay (WTP) . And contingent valuation method (CVM)
Introduction Climate change is becoming an ever more important issue in our lives. Presently, it is the leading global environmental problem. Extreme weather conditions, unexpected temperature and rainfall fluctuations are among the serious consequences of climate change and present a significant risk, particularly to agro-based economies (Tubiello et al. 2007; Georgescu et al. 2011). Most developing countries have agrarian economies that depend heavily upon agriculture, which in turn is the most vulnerable sector to climate changes (IPCC 2011). The climatic factors that influence agricultural productivity include rainfall patterns, temperature hikes, changes in sowing and harvesting dates, water availability, evapotranspiration and land suitability. There are serious concerns for the sustainable productivity of agricultural production in South Asia curtsy of abiotic and biotic stress. An abiotic climate change is a major factor threatening food security of the burgeoning population of South Asia, particularly Pakistan (Sultana et al. 2009). Similar findings were reported in recent IPCC publications and reports (IPCC 2007). Despite having climate diversifications in Pakistan, wheat is grown almost all over Pakistan and constitutes the staple food crop. This includes the arid regions of central and southern parts of Pakistan which are also considered food granaries (Klein et al. 2006). Since wheat is the staple food crop in
Environ Sci Pollut Res
Pakistan, the estimated land on which it is cultivated is 8693 ha, with a per hectare wheat yield of 2787 kg/ha. Per head wheat consumption in Pakistan is 124 kg/year where wheat flour supplies some 72 % of daily caloric intake per capita which is the largest in the world. These characteristics peculiar to wheat distinguish it from other food crops in Pakistan (GoP 2013). Figure 1 shows Pakistani wheat production in the last 5 years. Over the past few decades, wheat productivity increased extravagantly due to green revaluation. However, due to recent climatic variations, Pakistan’s arid zones are facing severe water stress, which has significantly damaged human life and economic activities (particularly agriculture). Pakistan has been experiencing growing temperatures resulting in extreme weather and climate variability. In view of agriculture’s dependence on climate factors (light, water and heat), crop yield will be significantly affected by climatic endorsed changes (World Meteorological Organization 1979). Wheat yield is also very sensitive to temperature increase. If the average temperature increases by 2.5 °C, wheat yield will reduce by 60 % (EIU, Pakistan Country Report 1994). The decrease in crop production in some regions is curtsy of prolonged dry spells and heat waves through moisture and thermal stress (Rounsevell et al. 2006). It was proposed that wheat yields will face serious climatic stress in the South Asian region. According to the fourth IPCC report, cereal yield in South Asia could decrease up to 30 % by 2050 coupled with a decline of gross per capita water availability from 1820 m3 in 2001 to 1140 m3 in 2050. In the case of Pakistan, water supply is scarce in many parts of the country. In the near future, a dramatic decline in water availability would result in a sharp decline in agricultural productivity. Thus, there is an urgent need to understand the impact of possible climate change in order to minimise such potential effects in addition to preparing for such an outcome (Sultana et al. 2009). Although climate change poses a serious threat to livelihoods and the economy, many farmers appear to have little Fig. 1 Wheat production of Pakistan Source: Pakistan Bureau of Statistics
concern for it. This is probably due to ignorance of the severity of the issue, weak institutional capacity, limited environmental and adaptation issues and lack of local knowledge (Howden et al. 2007; Lindner et al. 2010; Kurukulasuriya and Rosenthal 2013). The significance of climate in wheat production demands a comprehensive and efficient adaption framework (which can help to adapt effects of climate change) to determine farmers’ perceptions and attitudes toward changing climate. Accordingly, to ascertain an efficient adaptation framework, there is a need to analyse the applicability of climate change adaptation policies. Doubtless, climate change directly and indirectly affects the social and economic sustainability of farmers. It can cause crop failures and lead to heightened costs of production thereby reducing incomes by reducing productivity and ultimately raising the seasonal unemployment rate (Siwar et al. 2009; Alam et al. 2011). Since farmers largely depend upon agriculture, when agricultural production decreases, with it their revenues. This reinforces the need of adaptation strategies for farmers because such failures may cause chronic losses even leading to social anarchy (Challinor et al. 2010). According to Stern (Stern 2006), we are in the eleventh hour and must take serious steps to address the looming risk of climate change on society. We require adaptation strategies with planned implementation programmes. Their structure must go beyond encountering the adverse effects of climate change and should encircle a large number of social, economic and technical environmental challenges (Weitzman 2009; Shove 2010). Any adaptation method must jointly consider socioeconomic and climate change. Practitioners need to focus on the relevancy of future climate change and future society rather than social norms. The formidable challenge for policy makers is to identify relevant adaptation strategies that are susceptible to the population. At the same time, they face the dilemma that the adaptation strategies to climate change cannot provide equal benefits to all regions and ethnic groups. However, to reduce short and long-term vulnerabilities of climate change, adaptation is imperative (IPCC 2007).
Environ Sci Pollut Res
Since Pakistan has already experienced significant changes in weather in the form of floods, droughts, heat waves and tropical cyclones (Dai et al. 2004; Trenberth 2011), to tackle the challenges posed by climate change, knowledge of farmer perceptions toward climate change and their choice of adaptation method, in addition to the barriers affecting their adaptation of climate change is mandatory. Adaptation to climate change is a vital instrument that can reduce the adverse effects of climate change and has the potential to promote sustainable development through enhancing the welfare of the poorest members of society (Moss et al. 2010). Through adaptation, we can reduce vulnerability, increase resilience, moderate the risk of climate impact on lives and livelihoods and take advantage of opportunities posed by actual or expected climate change. Against the above background, the major objective of the study is to explore farmer perceptions and adaptation strategies to climate change, particularly of those growing wheat in Pakistan. The specific objectives of the study are: 1. To estimate the willingness to pay (WTP) of farmers for a planned adaptation programme of climate change in Rahim Yar Khan Punjab, Pakistan (Cannel irrigated area with the highest wheat production). 2. To determine the factor influencing their WTP. The study of WTP of farmers is important especially given the fact that no such research thus far been conducted in Pakistan. Therefore, there is a pressing need to survey the behaviour and WTP of Pakistani farmers for a planned adaptation programme to climate change in Rahim Yar Khan District of Punjab, Pakistan. This study will assist in the development of a suitable alternative framework for combating the adverse effects of environmental change and in the articulation of adaptation policies. In order to develop a sound understanding of WTP for the proposed climate change adaptation programme and socioeconomic and motivation factors that influence farmers’ WTP, we test the following two hypotheses. H1: Farmers’ socioeconomic factors have a positive direct relation with their willingness to pay (WTP) for a planned climate adaptation programmes. H2: Motivation factors have a positive and significant relation with WTP for planned climate adaptation programmes. Contingent valuation method A contingent valuation (CV) method was employed to estimate the WTP of farmers as applied to environmental valuation (Carson 2012). Many studies employed contingent valuation method (CVM) to quantify the benefits of non-marketed environmental goods and attributes in such a way they can enter directly into cost benefit calculations predominantly in developed Western, North American and East Asian countries
(Carson et al. 2010). The CVM methodology highlights diverse issues like improvements in water quality and sanitation (Howard et al. 2010; Vörösmarty et al. 2010; Orgill et al. 2013), valuing forestry (Canadell and Raupach 2008; Gelo and Koch 2012; Mason et al. 2013), exposure to flood risk (Lantz, et al. 2012; Kellens et al. 2013), wetland conservation (Yoon 2009; Kaffashi et al. 2013; Turner 2013), offsetting carbon emissions and groundwater contamination, health economics (Georgiou and Turner 2012; Del Borghi et al. 2013; Everard et al. 2013; Andersson, et al. 2014), cultural economics (Carvalho et al. 2010; Wicker et al. 2012), transportation safety and economics (Hess et al. 2012; Kristiansen 2013) and a wide range of environmental services (Ojeda et al. 2008; Vo et al. 2012). Furthermore, studies regarding WTP are conducted in developed countries and mainly examine consumers’ WTP for renewable energy concentrating on environmental (Bain et al. 2012; Park, et al. 2013) and health and social effects (Curtis 2012; de Bekker‐Grob et al. 2012; Damigos et al. 2009; Bollen et al. 2010; Sovacool 2011). However, most of these studies used CVM and mainly relied on choice experiments in recent times. Despite the diversity of study designs, all of them unanimously affirm that consumers generally have a positive attitude in terms of WTP. In this paper, our goal is to elicit what farmers in Rahim Yar Khan Punjab, Pakistan, are willing to pay for a planned adaptation programme to climate change. Survey questionnaires were used to ascertain the “willingness to pay” or “willingness to accept” (respectively), which means that the accuracy of contingent valuation methods depends on the quality of the survey instrument and how well people responded to the required assessments. The information component of the survey instrument or the CV scenario description of the item that is to be valued, the explanation of the method of provision, payment vehicle, the decision rule, and the period of payment (Boyle 2003) were presented to the farmers through a payment card. In this study, we developed a payment card according to Rowe et al. (1996). A payment card lists a series of values from which respondents choose an amount that best represents their maximum WTP. Payment card was designed with an exponential response scale that contains 24 cells. An exponential response scale is consistent with this hypothesis of measurement error increasing with WTP values. The values of the cells 2 through to 22 are computed (Table 1) by the following equation: Bn ¼ B1 ð1 þ K Þn−1
where, Bn =bid value, B1 =1, K=range selected for the payment card so that the largest value on the payment card is (1.2860)21 =200.
Environ Sci Pollut Res Table 1
Payment card
PKR0 ($ 0) PKR0.50 ($0.005) PKR1 ($0.010)*
Range PKR0–PKR 200 ($0–2.04)
Centre PKR 16 ($0.16)
PKR 2 ($0.020) PKR 3 ($0.030) PKR 4 ($0.041)
PKR 10 ($0.10) PKR 12 ($0.12) PKR 16 ($0.16)
PKR490 ($0.051) PKR 6 ($0.061) PKR 8 ($0.081)
PKR 20 ($0.20) PKR 25 ($0.26) PKR 35 ($0.36)
Function 1.2860 (n−1) PKR 45 ($0.46) PKR 55 ($0.56) PKR 75 ($0.77)
PKR 95 ($0.97) PKR 120 ($1.22) PKR 155 ($1.58)
PKR 200 ($2.04) ≥PKR 200 ($2.04) Don’t know
*$1=PKR 98
The payment vehicle was the electricity bill of the respondents. The fund will be managed by the climate change adaptation fund for agriculture, a non-government organisation (NGO) established specifically for that purpose.
Methodology Site selection and data collection The questionnaire used in this study was based on a survey among farmers of the canal-irrigated area of Rahim Yar Khan Punjab, Pakistan, in the southern part of Rahim Yar Khan as shown in Fig. 2. It covers approximately 1,188,000 ha, of which 795,000 ha is irrigated area. Approximately 254, 000 ha is allocated for wheat, 89,000 ha for sugarcane and 24,030 ha for wheat production.
Fig. 2 Map of Rahim Yar Khan Punjab, Pakistan
Survey design and sampling methods The study was conducted through direct face-to-face interviews in order to obtain reliable responses from the respondents. The study area is located in the southern part of Rahim Yar Khan which contains five wheat cultivated localities viz.: (1) Bhong, (2) Hamidabad, (3) Machhka, (4) Rahim Yar Khan, and (5) TarindaSawae-Khan of district Rahim Yar Khan all within a radius of 10 km of Rahim Yar Khan City were surveyed between January to February 2002. Of the eight areas and using a random sampling method, 80 farmers were selected with a total sample size of 400 (80*5). The survey was conducted in September, 2013. The survey was confined to within Rahim Yar Khan Punjab, as it constitutes a prominent agricultural zone in Pakistan. The data was collected through interviews with heads of households who worked as wheat farmers.
Environ Sci Pollut Res
Design of the questionnaire The questionnaire consisted of sections A, B and C. Section A collected information on the farmers’ socioeconomic characteristics (i.e. household size, gender, ethnic group, religion, education level, income, type of farmers, farm size and family size of the respondents). Section B enquired as to farmers’ perceptions and attitudes toward climate change. Section C consisted of CVM questions to estimate farmers’ WTP for a planned adaptation programme to climate change. This section also included some motivational factors that motivate them for WTP for climate change adaptation. In this section, farmers were asked to rank the motivation factors, which they feel are very important to motivate them for WTP for climate change adaptation. The most important item was to be ranked 1, the second most important 2, and so on. All items had to be ranked and no rank could be used more than once. Having collected the data from 283 farmers, we ranked all the motivating factors based on mean values. The lowest mean value was assigned the rank 1, which indicates the most important factor. Methods of data analysis The statistical analysis was carried out using SPSS. The analysis of this paper starts with respondents’ demographic profile followed by descriptive statistics of socioeconomic characteristics. A statistical analysis was employed to analyse the socioeconomics and motivation factors which influence farmer’s WTP for climate change adaptation. Chi-square tests also were administered to explore significant relationships between the amount of WTP bids and the factors used in the model. The χ2 value was employed to evaluate the influence of each factor on WTP with 0.05 used as the level of significance.
Specification of the research model A regression model was developed to explore the factors that might affect the WTP of farmers for a planned adaptation programme to climate in Rahim Yar Khan Punjab, Pakistan. Respondents were offered a yes/no option to select their WTP for a planned adaptation programme to climate change. When the dependent variable is in the 0–1 style, researchers can choose between logistic regression and probit regression (Wang et al. 2007). For this reason, in this study, logistic regression was selected as the evaluation method. It was assumed that the factors listed in Table 6 might affect WTP and these factors were included in the model as independent
variables. The probability model of the WTP is P(Yi =1), which was represented as: The basic model of the logit estimation is as follows: 3 2 h π i ; ……X p 7 6 PðY ¼ 1ÞjX 1 Loge 4 ¼ Log 5 e 1−π 1−P Y ¼ 1X 1 ; ……:X p ¼ α þ β1 X 1 þ ……… þ β p X p ¼ α þ
p X
β jX j
j¼1
π is a conditional probability of the form P(Y=1|X1,…,Xp). That is, it is assumed that success is more or less likely depending on a combination of values of the predictor variables. The log odd, as defined above, is also known as the logit transformation of π and the analytical approach described here is sometimes known as logit analysis. The logistic function takes the form of: Xp αþ β Xj D E j¼1 j e P Y ¼ 1X 1 ; ……X p ¼ Xp 1þe
αþ
j¼1
β jX j
This can also be transformed into: D E 1 P Y ¼ 1X 1 ; ……X p ¼ Xp 1þe
−α−
j¼1
β jX j
The non-response probability is P Y ¼ 0jX 1 ::::::X p ¼ 1−p Y ¼ 1jX 1 ::::::X p
1
¼
p
1þe
aþ∑ j¼1 β j X j
‘Yes’ (=1) if the farmers state a positive WTP and ‘No’ (=0) when they are not WTP any amount. The independent variables employed to predict the probability of WTP is age, education, household income, farm size and motivational factors such as ‘the environment has the right to be protected irrespective of the costs’, ‘I care about the environment in general’, ‘I feel responsible for my contribution to climate change’, ‘impacts on agricultural production’ and ‘concern for the risk posed by climate change’. Using the set of predictors, the LR equation for the log odds in favour of WTP is estimated to be: p log ¼ bo þ bi þ x i 1−p
Environ Sci Pollut Res Table 2
Variables included in the logit models
Variables
Description of the variables Category
Dependent variable: WTP
WTP for climate change adaptation
Independent variables Education Household size Farm size Income
The environment has the right to be protected irrespective of the costs I care about the environment in general I feel responsible for my contribution to climate change Impacts on agricultural production Concern for the risk posed by climate change
1=Willing to pay 0=Not willing to pay
Educational status
1=No formal education, 2=Primary, 3=Secondary, 4=Tertiary, 5=University Number of household members Area of farm Monthly family income 1=PKR 2000 ($20) and less, 2=PKR 2001 ($20)–4000 ($40), 3=PKR 4001 ($40)-6000 ($61), 4=PKR 6001 ($61)–8000 ($82), 5=PKR 8001 ($82)–10,000 ($102) and above PKR 10,000 ($102) Motivation factors for WTP 1=Strongly Disagree, 2=Disagree, 3=Neutral, 4=Agree and 5=Strongly Agree
1=Strongly Disagree, 2=Disagree, 3=Neutral, 4=Agree and 5=Strongly Agree 1=Strongly Disagree, 2=Disagree, 3=Neutral, 4=Agree and 5=Strongly Agree 1=Strongly Disagree, 2=Disagree, 3=Neutral, 4=Agree and 5=Strongly Agree 1=Strongly Disagree, 2=Disagree, 3=Neutral, 4=Agree and 5=Strongly Agree
Using the partial coefficients, bi, informing the change to log odds of agreeing to pay for a planned adaptation programme to climate change in the canal-irrigated area of Rahim Yar Khan Punjab, Pakistan. The independent and dependent variables used in the logit analysis and their basic statistics are
given in Table 2. A statistical analysis was employed to analyse the socioeconomics and motivation factors which influence farmer’s WTP for climate change adaptation.
Results and discussions Table 3
Socioeconomic characteristics of the respondents (n=385)
Demographics
Frequency
Percentage
Age 18–30 years
32
7.02
170 176 22
50.38 38.96 3.64
56 150 104 82 8
11.95 18.96 44.94 21.30 2.85
20 140 110 75 25 30 400
7.27 33.77 29.87 20.78 5.71 2.60 100
31–45 years 46–60 years 60 years and above Education level No formal education Primary Secondary Tertiary University Income
