1. Drivers of water conservation policies in rural and municipal systems: Results of a regional survey Damian C. Adams and Chris N. Boyer Oklahoma State University Department of Agricultural Economics Departmental Seminar February 19, 2010

2. Background and problem statement  Water supply problems in Southeastern US  No longer an urban city or ‘dry state’ problem  Droughts, population growth, diminishing access, other persistent factors (Dziegielewski and Kiefer, 2008).  Rural and small water utilities considering:  Price-based conservation (PC) measures that encourage conservation through consumers’ water bills  Non-price conservation (NPC) measures that reduce water demand or reduce waste (Olmsted and Stavins, 2008)

3. Price-based conservation

4. Non-price conservation  New or smart meters  Mandatory or voluntary watering restrictions  Education/awareness  Leak detection  Water budgets/audits  Incentives for efficient irrigation systems  Xeriscaping  Rebates/retrofits

5. Background and problem statement  Use of water conservation tools is largely unknown in the southern United States.  Small and rural utilities ignored by the literature:  Past studies provide little insight for non-urban utilities (e.g., USGAO, 2000)  Past studies fail to consider water managers’ attitudes and perceptions about water conservation, which can drive the adoption decision (e.g., Inman and Jeffrey, 2006).

6. Project overview  Survey of water supply managers in 4 Southeastern states: Oklahoma, Arkansas, Florida and Tennessee  Objectives (1) Identify use of water conservation tools in small water systems in Southeastern states (2) Identify barriers to price and non-price conservation programs by water systems (3) Evaluate factors affecting water conservation strategy (PC, NPC) use  Funded by Oklahoma Water Resources Research Institute and the USDA National Water Program

7. Survey design  Questions cover six categories:  System characteristics/demographics  Planning and investment  Notification and approval  Price conservation programs  Non-price conservation programs  Consequences and barriers to conservation  Expert review and pre-test (n=82)

8. Survey method  Dillman (2008) survey method:  Pre-survey introduction email  Two online survey emails  Reminder emails  Final online survey email  Pre-hardcopy postcard  Cover letter and hardcopy survey  Reminder postcard  Final hardcopy survey

9. Response rates StateResponsesSystems Response Rate Oklahoma29250058% Arkansas14939538% Florida15530651% Tennessee9921247% Total695141349%  87% of respondents completed the online version  Rural coverage bias for online survey not found (Boyer et al., forthcoming)

10. Survey Results

11. Utility size – by state

12. Ownership type – by size

13. Water use – by size

14. Water source – by state

15. Water source – by size

16. Changes in water demand (per-capita)

17. Conservation use – by state

18. Conservation use – by size

19. Type of non-price conservation

20. Factors impacting demand

21. Utilities’ plans to meet future demand

22. Perceptions on climate change Climate change will negatively and seriously impact supply Yes23% No29% Not sure39% Plans for responding to climate change Conservation program14% Repair infrastructure5% New supplies16% No plan/Studying14% Alternative supplies3%

23. Perceptions of elasticity  10% indicated that demand would be elastic (10% or more change in demand per 10% increase in price)  Residential customers typically respond to a 10% increase in water rates with a 1% - 3% reduction in water usage (AWWA, 2000)

24. Barriers to conservation

25. Barriers to conservation – key differences by size

26. Factors affecting conservation

27. Drivers of Water Conservation Strategy – Bivariate Probit Model  Probability of adopting PC and NPC, given demographics, etc: Φ 2 is the bivariate standard normal cumulative distribution function x is a matrix of independent variables, β PC and β NPC are vectors of parameter estimates, and ρ is the correlation between the equations for PC and NPC.  Allows direct examination of correlation between price and non-price conservation use

28. Model statistics Price-based Conservation (PC) Non-price Conservation (NPC) M odel fit (Percent correctly predicted) 92.52%74.96% Model test statisticsStatisticP-value Log Likelihood-311.22- Likelihood ratio: χ 2 (84 d.f.) 770.34***0.0000 ρ (Relationship between PC and NPC) -0.04300.8227 LR test of rho = 0: χ 2 (1 d.f.) 0.05020.8227 * Significant at the 10% level; ** Significant at the 5% level; *** Significant at the 1% level. § Excludes insignificant variables, except two variables central to the study: climate change and Arkansas.

29. Dependent variable Price-based Conservation (PC) Non-price Conservation (NPC) Independent variable § CoefficientP-valueCoefficientP-value Demographics Florida0.786***0.0241.515***0.000 Oklahoma0.883**0.0010.031 0.920 Arkansas0.1920.5390.392 0.174 Small size (< 0.5 million gallons/day)0.297*0.065-0.236 0.310 Groundwater source0.434**0.033-0.462** 0.040 Has secondary source-0.756**0.004-1.241** 0.014 Government recommends cons. adoption-0.0070.9831.275*** 0.004 Management recommends cons. adoption-0.880**0.0390.162 0.692 Had a per-capita water use increase, last 5 yrs0.2790.122 -0.413*0.099 Notify customers of rate changes - website0.0410.870 0.898***0.002 Notify customers of rate changes - meeting-0.1020.538 -0.366*0.056 Notify customers of rate changes - mail out0.406**0.011 -0.0360.862

30. Attitudes and Perceptions Determining rate schedule - cost of delivery0.236**0.0280.190 0.129 Determining rate schedule - consumer waste0.0290.714-0.177* 0.053 Increased the average rate in last five years0.2190.1560.435** 0.022 Reason for past rate increase - treatment costs0.412**0.012-0.194 0.406 Reason for past rate increase - system maintenance 0.599**0.0360.126 0.705 Reason for past rate increase - conservation1.490***0.0001.067*** 0.009 Internally studied demand elasticity0.681**0.027-0.048 0.903 Believes users do not respond to price increases-0.1190.443-0.591*** 0.003 Climate change will not impact water supplies-0.0560.743-0.034 0.201 Dependent variable Price-based Conservation (PC) Non-price Conservation (NPC) Independent variable § CoefficientP-valueCoefficientP-value

31. Dependent variable Price-based Conservation (PC) Non-price Conservation (NPC) Independent variable § CoefficientP-valueCoefficientP-value Future Planning Meet future demand - alternative source0.565**0.0291.204*** 0.000 Meet future demand - infrastructure expansion/replacement 0.410**0.0160.127 0.499 Meet future demand - manage demand0.870***0.000-0.210 0.460 Barrier to meeting demand - treatment costs0.275*0.083-0.108 0.597 Barrier to meeting demand - consumer waste0.0010.940-0.026* 0.063 Barrier to meeting demand - inability to increase withdrawals from source -0.479*0.0840.847*** 0.006 Barrier to meeting demand - population growth0.1230.448-0.637*** 0.001

32. Notable implications  Lack of knowledge/resources a barrier to adopting conservation  Elasticity studies  Technical/staff resources  Significant educational opportunities  Use of conservation programs/pricing  Views on elasticity, revenue change, climate change, etc  Decision-making and information provision  Planning and barriers

33. Conclusion  Key differences by utility size  Use of conservation tools  Attitudes/perceptions of barriers, climate change, elasticity  Very different set of factors drive PC, NPC decisions - implications for policy  Demographics, attitudes and perceptions, and future planning successfully predict conservation strategies  Using model to evaluate feasible water conservation tools for rural and small systems given their characteristics and consumers’ willingness to adopt (future work)

34. Thank you

35. Discussion - Demographic variables  Florida and Oklahoma utilities more likely to adopt PC; Florida utilities more likely to adopt NPC.  Small utilities were likely to adopt PC. This could be due to rural utilities trying to maintain revenue streams as they lose customers or face increasing production costs.  Systems using groundwater were more likely to adopt PC and less likely to adopt NPC.  Having a secondary water source to meet demand decreases the likelihood of PC and NPC.  More likely to adopt NPC if a government agency normally makes recommendations.  Less likely to adopt if utility management is responsible for making recommendations.  Systems that rely on mail-outs are more likely to use PC; those that post their information online are more likely to adopt NPC.

36. Discussion - Attitudes and perceptions  Having increased average rates in the last five years increases odds of NP. These utilities could be using NPC since price increases are already being used to cover inflation and increasing costs.  Having increased rates to signal conservation, increases odds of adopting PC and NPC.  Higher likelihood of PC if past rate increases were due primarily to treatment costs and system maintenance. PC might help utilities cover costs of delivery and infrastructure repair and maintenance more effectively than uniform rates or declining block rates.  Internally measuring water demand elasticity increases the likelihood of PC. Measuring a price demand elasticity helps providers better understand impacts on their revenue stream, and suggests critical self-evaluation that might result in more efficiency gains.  Views of potential impacts from climate change on water supplies are not significant.

37. Discussion - Future planning  Planning to use alternative water source increases likelihood of PC and NPC.  Planning to expand or replace infrastructure increases likelihood of adopting PC.  Believing that higher treatment costs are a barrier to meeting future demand increases PC adoption, which also implies PC is viewed as more effective for covering costs.  Viewing consumer waste and population increases as primary barriers to meeting demand reduces the likelihood of adopting NPC. This might suggest that NPC is not effective at reducing per-capita consumption.