Understanding Fundamental Phenomena Affecting the Water Conservation Technology Adoption of Residential Consumers Using Agent-Based Modeling
Abstract
:1. Introduction
2. Background
2.1. Water Conservation Affordability
2.2. Water Price and Incentives
2.3. Education and Demographics
2.4. Household/Building Attributes
2.5. Social Network Influence
3. Significance
4. Methodology
4.1. Agent-Based Modeling
4.2. Theoretical Framework
4.3. Computational Simulation
4.4. Model Initialization and Implementation
5. Model Verification and Validation
6. Scenario Setting
7. Results and Discussion
7.1. Socioeconomic Scenario Analysis
7.2. Social Network Influence Examination
7.3. Scenario Landscape Analysis
8. Concluding Remarks
9. Limitations and Future Studies
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Variable | Value | Coefficient | Distribution Type |
---|---|---|---|
Education: | |||
High school or less | If Yes = 1, if No = 0 | 1.92 | Real data |
Some college | If Yes = 1, if No = 0 | 2.58 | |
College graduate | If Yes = 1, if No = 0 | 2.91 | |
Advanced degree | If Yes = 1, if No = 0 | 4.39 | |
Income | |||
Less than $40,000 | If Yes = 1, if No = 0 | 0 | Real data |
$40,000–$75,000 | If Yes = 1, if No = 0 | 1.07 | |
Above $75,000 | If Yes = 1, if No = 0 | 1.58 | |
Home ownership | Owner = 1, Renter = 0 | 1.84 | Real data |
Head gender | Female = 1, Male = 0 | 1.21 | Random |
Resident (head) age | Years | 1.01 | Histogram |
House size | Square feet | 1 | Uniform (70; 56,000) |
Garden size | Square feet | 1 | Uniform (0; 8000) |
House age | Years | 0.99 | Random (1100) |
Household size | Numbers | 0.98 | Real data |
Technology | Price ($) | Potential Rebate ($) | Expected Water Savings (Gal/Day/Capita) | Category |
---|---|---|---|---|
Bathroom faucet | 15 | 15 | 0.57 | Inexpensive |
Kitchen faucet | 15 | 15 | 2.8 | Inexpensive |
Showerhead | 100 | 25 | 4.85 | Inexpensive |
Toilet | 420 | 50 | 1.63 | Expensive |
Washing machine | 670 | 150 | 6.91 | Expensive |
Dishwasher | 500 | 50 | 0.35 | Expensive |
Network Structure | Attribute | Parameter | Parameter values |
---|---|---|---|
Random | Assigns each agent a random number of connections within the given average. | Average number of connections per agent (N) | N = 0–10 |
Distance-based | If the distance between two agents is less than the given maximum connection range (the maximum distance in meters between agents for there to be a connection), then both agents are connected. | Maximum connection ranges (R) | R = 0–500 |
Ring lattice | Agents are connected according to their closeness to each other while also forming a ring. | Average number of connections per agent (N) | N = 0–10 |
Small-world | Connections between agents are similar to the ring lattice, while also including some long-distance relationships. The neighbor link probability is the chance that two agents connected to the same neighbor may also connect to each other. | Average number of connections per agent (N); and Neighbor link probability (P) | N = 0–10 P = 0–1 |
Scale-free | Some agents have multiple connections (considered as hubs), while others have very few connections. | Number of hubs (M) | M = 1–10 |
Parameter | Use | Method | Input Unit Changes |
---|---|---|---|
Household | Agent | Estimation of consumption; Influence diffusion | No change; 280 agents were used throughout the experimentation process |
Water price strategy | Input parameter | Fixed price; fixed charge; block tariffs | Nominal |
Rebate status | Input parameter | Rebate; no rebate | Nominal |
Social network structure | Input parameter | Random, distance-based, ring lattice, small world, scale-free | Nominal |
Likelihood of adoption due to social network | Input parameter | Function of randomTrue (p), given the likelihood p; True/False result | 1, 5, 10, 15, …, 100% |
Income growth | Input parameter | Change in annual income | −5, −4, …, 0, 1, …, 5% |
Household size growth | Input parameter | Change in household size | −5, −4, …, 0, 1, …, 5% |
Utility threshold | Input parameter | Accumulation of attributes influencing the potential for technology adoption (Utility > Threshold) | 10,000; 20,000; 30,000; 40,000; 50,000 |
Affordability threshold | Input parameter | Household ability to pay water expenditures (annual water bill + technology cost) | 1, 1.5, 2, 2.5, 3% |
Percent adopter | Output parameter | Percentage of agents that adopted at least one water conservation technology | (Changes in the outputs are a reflection of changes in the input parameters) |
Demand reduction | Output parameter | per household | |
Kitchen faucet | Output parameter | Number of kitchen faucets adopted | |
Bathroom faucet | Output parameter | Number of bathroom faucets adopted | |
Shower head | Output parameter | Number of shower heads adopted | |
Toilet | Output parameter | Number of toilets adopted | |
Washing machine (clothes) | Output parameter | Number of washing machines adopted | |
Dishwasher | Output parameter | Number of dishwashers adopted |
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Aspect of Technology Adoption | Findings of the Model | Examples of Other Studies with Similar Findings |
---|---|---|
Impact of conservation technology adoption on water demand reduction of the service area | Adoption of water conservation technology under various scenarios potentially could lead to a 3–10% reduction in the overall demand of the City of Miami Beach. | About a 6–14% reduction in water demand has been observed during the implementation of the water conservation incentives program for the residential consumers in Miami-Dade [68] |
Effect of water price strategy | Fixed charge strategy of water pricing, which provides cheaper water for households, led to a greater number of adoptions in the model. | “Pricing structure plays a significant role in influencing price responsiveness” [69]. The higher the price of water, the less technology one would adopt; conversely, the lower the price of water, the more technology one would install [28]. |
Effect of rebate and incentives | Rebate allocation in low-income communities could increase the adoption of the expensive water conservation technologies. | Providing incentives such as rebates for retrofitting households with water-efficient technologies have shown mixed results in terms of reducing water use, especially when compared to price-based approaches [13] |
Effect of social networks | Social interactions speeded up the diffusion of water conservation technology. Although the structure of a network was not important in the adoption of technology, it affected the time required for the adoption rate to reach an equilibrium. | “Social network type is not significant in determining mean energy use change, but is when considering the time required the network to reach equilibrium” [40]. |
Effect of household income level | Income growth mostly influences a household’s willingness to adopt water conservation technology. | “We have previously found financial variables to be important supplements to attitude measures in technology adoption modeling” [30]. |
Model Input Parameter | Possible Values | Value in Base Scenario |
---|---|---|
Water pricing structure | Fixed price; fixed charge; block prices | Fixed price |
Rebate status | Rebate; no rebate | No rebate |
Income growth (%) | −5; −4; −3; −2; −1; 0; 1; 2; 3; 4; 5 | 0 |
Household size growth (%) | −5; −4; −3; −2; −1; 0; 1; 2; 3; 4; 5 | 0 |
Utility threshold | 10,000; 20,000; 30,000; 40,000; 50,000 | 30,000 |
Affordability threshold (%) | 1, 1.5, 2, 2.5, 3 | 1.5 |
Social network structure | Random (N = 1); distance-based (R = 100); ring lattice (N = 1); scale-free (M = 1); small-world (N = 1, P = 0.1) | Random (N = 1) |
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Rasoulkhani, K.; Logasa, B.; Presa Reyes, M.; Mostafavi, A. Understanding Fundamental Phenomena Affecting the Water Conservation Technology Adoption of Residential Consumers Using Agent-Based Modeling. Water 2018, 10, 993. https://doi.org/10.3390/w10080993
Rasoulkhani K, Logasa B, Presa Reyes M, Mostafavi A. Understanding Fundamental Phenomena Affecting the Water Conservation Technology Adoption of Residential Consumers Using Agent-Based Modeling. Water. 2018; 10(8):993. https://doi.org/10.3390/w10080993
Chicago/Turabian StyleRasoulkhani, Kambiz, Brianne Logasa, Maria Presa Reyes, and Ali Mostafavi. 2018. "Understanding Fundamental Phenomena Affecting the Water Conservation Technology Adoption of Residential Consumers Using Agent-Based Modeling" Water 10, no. 8: 993. https://doi.org/10.3390/w10080993