Increasing attention has been paid worldwide to the issue of water conservation in the context of climate change and food security. The European Union (EU) has been approaching this in a number of ways, such as the Water Framework Directive, progressing towards an integrated approach to freshwater management, with the goal of achieving 'good status' for all EU waters by 2015. Many EU member countries have adopted River Basin Management Plans for 2009 - 2015 to improve management of water resources. The 'Blueprint to Safeguard Europe's Water Resources' is being developed as the EU policy response to the continuing challenge of delivering the EU's water policy goals. The analysis underpinning the Blueprint is expected to drive EU water policy over the long term (2050) and it focuses on a number of issues including household water consumption and waste water (European Commission, 2012b). Understanding which determinants influence water conservation behaviour and perceptions towards improving the efficiency of water use has been the focus of a number of research studies over time and increasingly so during the past couple of decades (Nieswiadomy, 1992; Dandy et al., 1997; Renwick and Archibald, 1998; Renwick and Green, 2000; Campbell et al., 2004; Syme et al., 2004; Gilg et al., 2005). This study analyses the impact that knowledge (amongst other a priori determinants) has on the stated water conservation perceptions and behaviour of citizens from 27 EU members. We applied a behavioural economics approach using an Eurobarometer dataset (European Commission, 2012a) and structural equation modelling (SEM). SEM is a statistical tool testing the strength of causal relationships, i.e., how much factors influence one another and primarily the perceptions towards water issues. The model consists of two parts, namely the measurement model (which specifies the relationships between the latent variables and their constituent indicators), and the structural model (which designates the causal relationships between the latent variables). We performed model estimation with the Diagonally Weighted Least Squares (DWLS) method using the statistical package Lisrel 8.80 (JÃ¶reskog and SÃ¶rbom, 2007). DWLS estimation method is consistent with the types of variables included in the model (ordinal and categorical) and the deviation from normality in some of these variables (Finney and DiStefano, 2006). We selected datasets for 27 EU countries (average sample size of 945 observations) and the variables included in the analysis were: * Socio-demographic variables (age, education, occupation, gender, number of children living in the household; place of living -- rural or urban); * Knowledge variables (perceived level of knowledge about problems facing groundwater, lakes, rivers and coastal waters; awareness of the current EU water policy, i.e., the Blueprint to Safeguard Europe's Water Resources; awareness of the water policy at EU member level, i.e., River Basin Management Plans; having taken part in River Basin Management Plan consultations); * Perceptions about water problems (seriousness of water-related problems such as water quality, floods, droughts and overconsumption of water; changes in the quality of groundwater, rivers, lakes and coastal waters; main threats, such as water shortage, to the water environment; impact of various sectors and activities on water status); * Perceptions about the impact of household water consumption and waste water on the status (quality and quantity) of water; * Perceptions about measures to induce water conservation behaviour (charging users for the volume of water they use; pricing water to reflect the environmental impact of water use; implementing a fair water pricing policy; introducing heavier fines for offenders; providing more information on the environmental consequences of water use; ensuring higher financial incentives, such as tax breaks and subsidies for efficient water use; ensuring better enforcement of existing water legislation; introducing stricter water legislation; increasing taxation on water-damaging activities); * Water conservation behaviour (reducing water use, using eco-friendly household chemicals, avoiding the use of pesticides and fertilizers for gardening, harvesting rain water, choosing organic farming products, recycling household oil waste, unused pharmaceuticals, unused household chemicals, paints, solvents, batteries). The model has a good fit according to the measures of absolute, incremental and parsimonious fit (Hair et al., 2006). The standardized structural coefficients for both practical and theoretical implications were examined. Results show that, alongside other determinants, knowledge will significantly impact water conservation perceptions and behaviour in each of the countries studied, confirming findings from the literature (Syme et al., 1990--1991; De Young, 1996; Corral-Verdugo et al., 2002; Syme et al., 2004) and emphasizing once again the importance of information to enhance public knowledge of water issues. This might suggest the need for the European Union to invest more in enhancing the water conservation information available to the public and improving access to it through measures such as water conservation education campaigns (European Commission, 2012b). In recent years the amount of information on water issues available to public has increased considerably, however there is a need for ample, clear, sufficiently strong, and consistent signals. Policy-makers should ensure an efficient knowledge transfer to the public and subsequently facilitate their informed response. Campbell, H.E., Johnson, R.M., Larson, E.H., 2004. Prices, devices, people, or rules: the relative effectiveness of policy instruments in water conservation. Review of Policy Research 21, 637--662. Corral-Verdugo, V., Frias-Armenta, M., Perez-Urias, F., Orduna-Cabrera, V., Espinoza-Gallego, N., 2002. Residential water consumption, motivation for conserving water and the continuing tragedy of the commons. Environmental Management 30, 527--535. Dandy, G., Nguyen, T., Davies, C., 1997. Estimating residential water demand in the presence of free allowances. Land Economics 73, 125--139. De Young, R., 1996. Some psychological aspects of reduced consumption behavior: the role of intrinsic satisfaction and competence motivation. Environment and Behaviour 28, 358--409. European Commission, 2012a. Flash Eurobarometer 344 Attitudes of Europeans towards water-related issues. Brussels, March 2012. European Commission, 2012b. Report on the Review of the European Water Scarcity and Droughts Policy. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. European Commission, Brussels. Finney, S.J. & DiStefano, C. (2006). Non-normal and Categorical data in structural equation modeling. In G. r. Hancock & R. O. Mueller (Hrsg.). Structural equation modeling: a second course (S. 269--314). Greenwich, Connecticut: Information Age Publishing. Gilg, A., Barr, S., Ford, N., 2005. Green consumption or sustainable lifestyles? Identifying the sustainable consumer. Futures 37, 481--504. Hair, J. F., Black, W., Babin, B., Anderson, R.E., and Tatham, R.L. 2006. Multivariate data analysis. 6th edition, Upper Saddle River, NJ: Pearson Prentice Hall. JÃ¶reskog, K. G., and SÃ¶rbom, D. 2007. LISREL8.80: structural equation modeling with the SIMPLIS command language. Chicago, USA: IL Scientific Software International. Nieswiadomy, M.L., 1992. Estimating Urban Residential Water Demand: Effects of Price Structure, Conservation, and Education. Water Resources Research 28, 609--615. Renwick, M.E., Archibald, S.O., 1998. Demand side management policies for residential water use: who bears the conservation burden? Land Economics 74, 343--360 Renwick, M.E., Green, R.D., 2000. Do residential water demand side management policies measure up? An analysis of eight California water agencies. Journal of Environmental Economics and Management 40, 37--55. Syme, G.J., Seligman, C., Thomas, J.F., 1990--1991. Predicting water consumption from homeowners' attitudes. Journal of Environmental Systems 20, 157--168. Syme, G.J., Shao, Q., Po, M., Campbell, E., 2004. Predicting and understanding home garden water use. Landscape and Urban Planning 68, 121--128.