Willingly or grudgingly? A meta-analysis on the willingness-to-pay for renewable energy use
Introduction
Renewable energy sources (RES) look set to take over as the future principal source of energy. Based on the latest statistics by the U.S. Energy Information Administration, in 2011, electricity consumption from RES in North and South America totals to about 1750 billion kilowatthours (bkW h), Europe 933 bkW h, Africa 115 bkW h, Middle East 20 bkW h, and Asia 1336 bkW h (with China consuming the largest proportion of it, at 800 bkW h). In June 2014, the U.S. Environmental Protection Agency has proposed a Clean Power Plan, which aims to reduce U.S. carbon pollution by 30% in 2030. The 2012 United Nations General Assembly has declared 2014–2024 the ‘U.N. Decade of Sustainable Energy for All’, with a global action plan to mainly enable the use of sustainable and renewable energy. In 2009, the European Union (EU) issued a Renewable Energy Directive in which member countries are mandated to achieve a 20% overall share of renewable energy by 2020 and by 2010 the EU is on its target trajectory path with a renewable share of 12.7%. The Kyoto Protocol, agreed upon in 1997 and going into effect in 2005, commits participating countries to reduce their greenhouse gas emissions by 5.2% from their 1990 level by the 2008–2012 target period [1]. While it is helpful to outline such idealistic global and regional goals, it ultimately boils down to how willing households are in migrating from conventional fossil energy source to RES use.
Against this backdrop, many valuation studies have been done on the willingness-to-pay (WTP) for RES use, but there seems to be no consensus among the studies as to how much households are actually willing to pay. Results from these individual studies are often inconclusive or even contradictory, with considerable variations in the magnitude, sign, and significance of their WTP estimates. To further complicate comparisons, the studies are conducted in different years and countries, focusing on different types of RES, measuring WTP in different currencies and temporal units, and using different elicitation formats to derive WTP. All these factors contribute to the differences in the primary studies’ estimated WTP for RES. In general, the paper aims to shed lights on why some households are willing to pay more for RES use and why some are less so.
A meta-analysis is therefore well-suited in consolidating findings from these studies and to answer the following questions. How much are households willing to pay for RES use, on average? What are the determinants of the differences in the WTP estimates obtained across studies? Is there bias in reporting, i.e. publishing only significantly positive WTP values? In answering these research questions, this paper has three specific objectives, one for each question.
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To obtain a summary estimate of the WTP.
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To identify sources of heterogeneity in the WTP.
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To check for publication bias.
Studies on RES have seen a marked increase in the most recent decade, with the majority of them in the US and European countries. Studies on RES are sparse in other continents. In the course of our literature search, we found only one related study in South America [2], and three in Africa [3], [4], [5]. The types of RES examined by these studies are usually generic, also termed as green or clean energy source. Some studies do however focus on a particular type of RES, for instance, on biomass energy [6], [7], [8], [9], hydro energy [10] and wind energy [11]. Most studies on RES are concerned with households’ WTP for RES use.
The contingent valuation method (CVM) is the most widely used method to derive WTP; a small minority used choice experiment (CE) [12], [13], [14]. Valuation of environmental-related goods and services can also be measured using other methods such as the travel cost and hedonic pricing methods. CVM is singled out as the most popular way to elicit willingness-to-pay because of its ability to capture nonuse values such as existence values. The question formats used to elicit WTP values are diverse, with the most popular being the dichotomous choice, single and multiple-bounded dichotomous choice. The cheap talk design developed by [15], though a unique and fairly new design in tackling hypothetical bias, it seems to be the format least used. CVM involves the use of questionnaires to elicit WTP for, usually, hypothetical projects or programs [16]. The CE valuation method on the other hand, takes into account public preferences and economic efficiency [17]. Following [18] however, we will consider the CE method an extension or a variant of the CVM, and that both methods give meaningfully comparable WTP since they use the stated preference approach. For the purpose of our study, we will also assume that the primary valuation studies have been conducted without biases such as information or strategic biases.
Meta-analysis has its roots in the field of educational research [19], [20]. The breakthrough of meta-analysis into economics is made by Stanley and Jarrell [21]. They went on to produce the first application of meta-analysis within economics in their study on the union–nonunion wage gap [22]. Following this, meta-analysis has been adopted in economic subfields such as education, and labor. Environmental-related meta-analyses are initiated by Smith and Kaoru on recreation benefits [23], along with Loomis and White on benefits of endangered species [24] (updated in [25]).
To our best knowledge, there is one work to date that is related to our current work. Sundt and Rehdanz [26] is a working paper on the WTP for RES use. They used 18 primary studies with 85 estimates in their meta-analysis. Our current work here, besides examining a larger pool of primary studies (30 primary studies with 137 WTP estimates), differs from [26] in a number of fundamental ways. The crucial differences are as follows.
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For reasons of comparability, we include only studies with mean WTP estimates.
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To ensure proper rigor of our meta-analysis, we (i) incorporate the variance estimates associated with the WTP estimates, (ii) use random-effect model in the weighting scheme and (iii) address the issue of multiple estimates through clustering of individual study.
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We include more recent studies.
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We check for presence of publication bias.
Out of the 18 primary studies included in [26], we exclude seven of them due to reasons of having no variance estimates [27], [28], WTP in terms of marginal estimates [29], [30], WTP in units unconvertible to the per household per month unit that we use [31], [32], and use of implicit instead of a precise WTP measure [33]. We believe our findings would complement those from [26] and vice-versa.
Our study contributes to the current RES literature in at least two ways. First, by synthesizing different findings of WTP values from the primary studies to obtain a summary WTP estimate of how much more households are willing to pay on average as compared to the use of conventional energy source, this could help give better indications whether RES targets are within reach. Second, by specifying a meta-regression model to identify sources of heterogeneity in the WTP obtained from primary studies, this model could be used to estimate the WTP values for RES use in different settings. The remainder of this paper is organized as follows. We provide the methodology set-up of the meta-analysis in Section 2, where we explain in details from the literature search to the construction of our meta-dataset. Section 3 presents and discusses the main findings, while Section 4 concludes.
Section snippets
Methodology
A meta-analysis is essentially a study of studies, with three main objectives. The first is to synthesize the often individually inconclusive and seemingly incoherent findings from a large number of studies, in order to come up with a summary effect size (a conventional term from the meta-analysis literature), i.e. in our case, a summary WTP estimate. The second objective is to identify the sources of heterogeneity in the WTP estimates obtained from primary studies, by using a meta-regression.
Descriptive statistics
It is imperative to get an overview of some of the important descriptive statistics of the 30 primary studies prior to analysis proper.
We can see from Table 1 that all the 30 studies are published on or after the year 2000. This goes the same with the years of survey with the exception of [45], [76]who carried out their survey in 1999 and 1996. The publication and survey years indicate that interest and awareness on RES are on the rise in the recent decade. Many of these studies examine the WTP
Conclusions
From the many RES targets outlined by different regional and global communities mentioned in Section 1, it is evident that the use of RES is inevitable. In order to know if the targets are on their right trajectory paths, we need to know how willing households are in shifting to RES use, which is typically gauged by their WTP. This paper provides a meta-analysis on the willingness-to-pay for the use of renewable energy source. We find that urban residents and North American households to have
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Senior Lecturer at School of Economics, Finance & Banking, University Utara Malaysia. Postal address: Economics Building, School of Economics, Finance & Banking, University Utara Malaysia, 06010 Sintok, Kedah, Malaysia.