Introduction
India’s geographical, economic, and cultural diversity—at both macro and micro levels—presents a highly complex environment for delivering viable, safe, affordable, and sustainable drinking water and sanitation services. Since the 1990s, there has been an ongoing discussion about involving the private sector in service delivery. While the transition to private sector involvement in power distribution and cost recovery has been relatively smooth and successful in many urban areas, the water and sanitation sector has posed greater challenges.
These challenges are further complicated by political interference, where elected officials often treat treated water as an electoral tool, distributing it freely under the assumption that those living in slums are too poor to pay for water delivered to their doorsteps. This practice undermines public funding, efficiency, and cost recovery.
However, my experience leading the water sector at the Danish International Development Agency from 1997-2005 revealed a different perspective:
- A. Even the rural poor value reliable and safe water.
- B. They can be successfully trained and engaged in every stage of a water scheme, from inception to operation and delivery.
- C. They are willing to pay for the operation and maintenance costs of water services, whether provided through standposts or direct doorstep supply.
Given India’s mixed track record in water governance, which has frequently placed undue strain on the public exchequer, there is growing interest in involving the private sector. However, pilot PPP projects, such as those in Nagpur and Mumbai, have yielded mixed results. This underscores the need for a more rational approach to selecting PPP interventions. In many cases, different types of PPP models may be more appropriate for different areas within the same city. To facilitate this, a well-structured Decision Support System (DSS) becomes essential for making informed and context-specific choices.
In this blog, I would be presenting a methodology to quantify score for decision making which is open to discussion and refinements.
Typical Decisions Required in case of PPP
Typically, it is required to take a decision among several possible interventions from the private sector is required for prioritizing the Type of PPP Intervention, viz.; Build-Operate Models (DBO, BOT, BOO), Operational & Service Contracts or the concession models or outcome-based performance.
Methodology
To create a quantitative SWOT based Decision Support tool for private sector intervention in water supply schemes across different settings like metros, Tier 2 cities, slums, rural areas, and more, we can assign numerical values (scores) to each element of the SWOT (Strengths, Weaknesses, Opportunities, Threats). These scores will reflect the relative importance or severity of each factor, allowing for a more objective assessment of each PPP model’s viability in various contexts.
Here’s how you could design this system:
Steps for the Quantitative Evaluation System:
- Define Evaluation Criteria for Each SWOT Factor: For each factor, assign scores based on criteria such as infrastructure complexity, cost recovery potential, socio-political context, and public health needs.
- Strengths and Opportunities are scored positively (e.g., 1–10).
- Weaknesses and Threats are scored negatively (e.g., -1 to -10).
- Contextual Adjustments: Assign weights to these factors based on specific settings (metros, rural areas, etc.) to account for contextual differences.
- Combine Scores: Aggregate the scores for each PPP model in each setting to determine which model is most viable in each case.
Criteria for Assigning Weights
In assigning the weights to Strengths, Weaknesses, Opportunities, and Threats (SWOT) for water supply schemes in different settings, several adjustments are required to be made to ensure the weights were contextually relevant and fair. These assumptions are outlined below:
1. Population and Socio-Economic Context
- Economically Better Off Areas in Metros: Each metro has metropolitan areas that have a larger, wealthier population with complex infrastructure needs. This is positive setting or Strength (such as private-sector expertise). However, these areas also face Threats like political resistance due to higher tariffs, making them significant but slightly less weighted.
- Tier 2 Cities and Small Towns: It is logical to assume that most of these settings have moderate infrastructure needs and a growing demand for water services, making Opportunities more critical due to the potential for expansion and investment. Strengths and Weaknesses are moderately weighted, reflecting a balance between affordability and infrastructure gaps.
- Slums & Unauthorized Settlements: These areas have low-income populations with minimal cost recovery potential, leading to higher emphasis on Weaknesses (e.g., affordability, service sustainability). I also assumed that Opportunities for investment and technological improvements are limited here, so they are given lower weights.
- Rural Areas: Rural populations provide a perfect setting for water supply and sanitation schemes using rudimentary technology on a demand driven and participatory basis. But for systems like PPP, they are perceived to present significant affordability challenges. The PPP models may struggle in these areas, especially regarding cost recovery. Therefore, Weaknesses and Threats are given higher weights, while Strengths (e.g., private sector efficiency) are less critical due to simpler infrastructure needs and reliance on community management.
2. Infrastructure Complexity and Investment Needs
- Metros: It is logical to consider that these areas require large-scale, complex infrastructure solutions, making Strengths (e.g., private expertise in managing large infrastructure) highly weighted. Opportunities for investment in advanced technologies (e.g., smart meters, large treatment plants) are assumed to be substantial.
- Tier 2 Cities: In these areas, infrastructure needs are growing but less complex than metros. This provides Opportunities for localized investments and efficiency improvements that can be highly weighted. It also seems right to assume that PPP models could address expanding demand, which influences the higher weight on Opportunities.
- Slums: It is assumed that infrastructure needs in slums are focused on basic water access and sanitation, making Weaknesses for intervention of private sector more prominent due to poor cost recovery potential and health risks. Strengths and Opportunities are given lower weights as advanced infrastructure solutions are not as relevant.
- Rural Areas: It is assumed that rural infrastructure is simpler, so private expertise (a Strength) is less necessary. The major challenge lies in affordability and community acceptance, which makes Weaknesses and Threats more significant.
3. Political and Social Resistance
- Metros and Slums: It is assumed that political and public resistance to tariff increases, and water privatization is higher in areas with larger populations and strong social equity concerns, such as metros and slums. This assumption led to a higher weight on Threats in these settings.
- Rural Areas: It assumed that rural areas would face political resistance due to water being seen as a social good, but this resistance might be slightly less intense than in urban areas. Therefore, Threats still carry weight but are not the highest priority compared to Weaknesses.
4. Government Support and Regulatory Environment
- Metros and Tier 2 Cities: It is assumed that government policies (e.g., Jal Jeevan Mission, Smart Cities) provide strong regulatory support for investment in large urban areas, which positively influences Opportunities. In these settings, regulatory oversight is expected to help manage private-sector involvement, reducing some of the risks associated with Threats.
- Slums and Rural Areas: It is assumed that regulatory frameworks are weaker or less effective, making PPP models more vulnerable to issues like corruption or inadequate oversight. This assumption led to a higher weight on Weaknesses and Threats in these contexts.
5. Technological and Financial Innovation
- Metros and Tier 2 Cities: It is considered that these settings are more open to adopting new technologies and innovative financing mechanisms, leading to higher Opportunities for technology transfer, efficiency improvements, and foreign investment. Therefore, Opportunities were given more weight in these settings.
- Slums and Rural Areas: It is considered that innovation and technology adoption would be more challenging due to limited financial resources, making Opportunities less significant. In these areas, community-driven solutions may be more effective than large-scale technological interventions, so Opportunities were weighted lower.
Summary of Assumptions made in assigning weights
- Infrastructure Complexity drives the higher weighting of Strengths and Opportunities in metros and Tier 2 cities.
- Cost Recovery Challenges and affordability issues lead to higher weights on Weaknesses and Threats in rural areas and slums.
- Political Resistance and social equity concerns increase the weight of Threats in densely populated areas like metros and slums.
- Government Support influences the weight of Opportunities in settings where policy frameworks (e.g., Jal Jeevan Mission) are strong, such as metros and Tier 2 cities.
- Technological Innovation is assumed to be more relevant in urban settings, increasing the weight of Opportunities in these areas.
These assumptions help create a structured approach to assigning weights in a way that accounts for the specific needs, challenges, and opportunities in different settings across India.
Explanation of the Score System:
- Strengths (1-10): Indicates how strongly the PPP model addresses key water infrastructure challenges.
- Weaknesses (-1 to -10): Reflects the potential drawbacks or costs associated with the PPP model.
- Opportunities (1-10): Represents the potential for technology adoption, investment, and efficiency improvements.
- Threats (-1 to -10): Accounts for political, social, and regulatory risks that could negatively impact the PPP model’s success.
Highest Scoring Models by Setting:
- Metros: Operational & Service Contracts (Score: 8)
- Tier 2 Cities: Operational & Service Contracts (Score: 10)
- Slums: Operational & Service Contracts (Score: 6)
- Small Towns: Operational & Service Contracts (Score: 10)
- Regional Water Schemes: Operational & Service Contracts (Score: 7)
- Rural Areas: Operational & Service Contracts (Score: 2)
To assign weights to the Strengths, Weaknesses, Opportunities, and Threats (SWOT) for water supply schemes in different settings while minimizing personal bias, it is important to use an objective, data-driven approach. The following steps and criteria can help ensure that the process remains fair, transparent, and consistent across all settings:
1. Contextual Relevance
Each setting (metros, Tier 2 cities, slums, small towns, regional water schemes, rural areas) has unique socio-economic, political, and geographical realities that influence the effectiveness of PPP models. Weights should reflect these contextual differences.
- Metros: Focus on infrastructure complexity, technological advancement, cost recovery potential, and political resistance.
- Rural Areas: Emphasize affordability, community engagement, and local governance structures.
- Slums: Focus on affordability, social equity, and health risks.
Assignment of weight is based on factors such as:
- Population Size: Larger populations (metros) tend to have more complex infrastructure needs but also more political and social pushback, while smaller populations (rural areas) face affordability and technical expertise challenges.
- Economic Status: Poorer populations, especially in slums and rural areas, may struggle with full cost recovery models but are able to afford a fee for basic facilities for their health, provided they are not confused by political class. The simple logic to this is that almost every house in urban slums affords DTH TV transmission service for their entertainment. They even buy water at a high cost that are supplied by the tanker mafia. Then why not charge full cost of operation and ensure basic intermittent supply with assured service?
- Regulatory Environment: Areas with stronger regulations (metros) may handle complex contracts better than rural or less regulated regions.
Below is a table with typical weights assigned to each Strength, Weakness, Opportunity, and Threat for Metros, Tier-2 Cities, Slum Areas, and Rural Areas. The total weights for each setting will sum up to 100.
| Setting | Strengths (%) | Weaknesses (%) | Opportunities (%) | Threats (%) | Total (%) |
| Metros | 35 | 20 | 25 | 20 | 100 |
| Tier-2 Cities | 30 | 20 | 30 | 20 | 100 |
| Slum Areas | 25 | 40 | 15 | 20 | 100 |
| Rural Areas | 20 | 35 | 25 | 20 | 100 |
Explanation of the Weight Assignment:
Metros:
- Strengths (35%): Metros benefit from strong private sector expertise, advanced technology, and larger investments, so strengths are weighted higher.
- Weaknesses (20%): While there are challenges like political interference and high initial costs, these are somewhat mitigated by stronger infrastructure and economic status.
- Opportunities (25%): There are significant opportunities for investment and innovation, particularly with the support of government initiatives like Smart Cities.
- Threats (20%): Political resistance to tariffs and public opposition still pose significant risks, but metros have more resources to address these issues.
Tier-2 Cities:
- Strengths (30%): Tier-2 cities have growing infrastructure needs and moderate access to private sector expertise, so strengths are slightly lower than in metros.
- Weaknesses (20%): Challenges exist but are balanced by ongoing urbanization and better infrastructure compared to rural areas.
- Opportunities (30%): There is great potential for scaling up infrastructure, attracting private investment, and digitizing water management systems, making opportunities a key factor.
- Threats (20%): There is still some political and regulatory risk, although less intense than in metros.
Slum Areas:
- Strengths (25%): Slum areas have lower infrastructure and financial capacity, which limits the impact of private sector strengths.
- Weaknesses (40%): Cost recovery, affordability, and political instability are significant concerns, giving weaknesses the highest weight.
- Opportunities (15%): Investment potential is low due to social and economic barriers, so opportunities are weighted lower.
- Threats (20%): There is notable risk of public opposition and failure due to poor regulation, making threats a significant consideration.
Rural Areas:
- Strengths (20%): Rural areas typically rely on simpler, community-driven systems, which makes private sector strengths less relevant.
- Weaknesses (35%): Affordability, cost recovery, and lack of local capacity are major barriers, so weaknesses are heavily weighted.
- Opportunities (25%): There is some opportunity for technological innovation (e.g., solar-powered pumps) and community engagement, though on a smaller scale.
- Threats (20%): Political and social resistance, as well as financial challenges, are key threats but not overwhelming compared to weaknesses.
2. Assignment of Score:
Use objective indicators to quantify each aspect of SWOT. Base the weightings on measurable data from sources like government reports, PPP performance in similar settings, and international benchmarks.
Strengths Criteria
- Technological Capability: Is there access to private-sector expertise that can significantly improve infrastructure? How well-established is this expertise in a given setting?
- Infrastructure Complexity: Does the setting require advanced infrastructure solutions (metros), or is it more localized and community-driven (rural areas)?
- Operational Efficiency: How effectively can private sector management improve efficiency in this context (e.g., metros may benefit more from operational efficiency than rural areas)?
Weaknesses Criteria
- Cost Recovery: Can the local population afford the tariffs needed to sustain a PPP model? High costs should receive higher weights in poorer areas.
- Contract Complexity: Are there governance or capacity limitations that make complex contracts difficult to implement or regulate?
- Service Continuity: How likely are service disruptions due to weaknesses like financial insolvency or inadequate oversight?
Opportunities Criteria
- Technology Adoption: Is there potential for technology transfer (e.g., smart meters in metros) or simple, community-driven innovations (e.g., solar pumps in rural areas)?
- Investment Potential: How attractive is the setting to private investors? This would be high in metros and low in rural areas, affecting the weight assigned.
- Regulatory Support: How much support is provided by the government for innovation in water supply (e.g., policies like the Jal Jeevan Mission)?
- Threats Criteria
- Political Resistance: How likely is political opposition to PPP models in the given setting (higher in slums and metros due to issues like tariffs and privatization)?
- Public Backlash: Are there social or civil society concerns over privatization of water in certain settings?
- Infrastructure Risks: Are there significant risks of failure due to poor maintenance or financial issues?
Example: In slums, Weaknesses might carry more weight (40%) due to affordability and limited cost recovery potential, whereas Opportunities might be less weighted (15%) because of lower investment potential and political resistance.
Evaluation Parameters and Scorecard
Quantitative SWOT Evaluation System for Water Supply Schemes
| PPP Model | Strengths | Weaknesses | Opportunities | Threats | Total Score |
| Metros | |||||
| Build-Operate Models (DBO, BOT, BOO) | 8 | -5 | 7 | -3 | 7 |
| Operational & Service Contracts | 6 | -4 | 8 | -2 | 8 |
| Concession Models | 7 | -6 | 6 | -3 | 4 |
| Outcome-Based Models (OBA) | 5 | -4 | 7 | -3 | 5 |
| Tier 2 Cities | |||||
| Build-Operate Models (DBO, BOT, BOO) | 7 | -4 | 8 | -3 | 8 |
| Operational & Service Contracts | 6 | -3 | 9 | -2 | 10 |
| Concession Models | 6 | -5 | 7 | -3 | 5 |
| Outcome-Based Models (OBA) | 5 | -4 | 7 | -4 | 4 |
| Slums and Unauthorized Settlements | |||||
| Build-Operate Models (DBO, BOT, BOO) | 5 | -6 | 6 | -7 | -2 |
| Operational & Service Contracts | 7 | -5 | 8 | -4 | 6 |
| Concession Models | 4 | -7 | 5 | -6 | -4 |
| Outcome-Based Models (OBA) | 6 | -5 | 7 | -5 | 3 |
| Small Towns | |||||
| Build-Operate Models (DBO, BOT, BOO) | 6 | -4 | 7 | -3 | 6 |
| Operational & Service Contracts | 8 | -4 | 8 | -2 | 10 |
| Concession Models | 6 | -6 | 6 | -4 | 2 |
| Outcome-Based Models (OBA) | 5 | -5 | 7 | -4 | 3 |
| Regional Water Supply Schemes | |||||
| Build-Operate Models (DBO, BOT, BOO) | 7 | -5 | 8 | -4 | 6 |
| Operational & Service Contracts | 6 | -4 | 8 | -3 | 7 |
| Concession Models | 7 | -6 | 6 | -4 | 3 |
| Outcome-Based Models (OBA) | 6 | -5 | 7 | -5 | 3 |
| Rural Water Supply Schemes | |||||
| Build-Operate Models (DBO, BOT, BOO) | 4 | -6 | 6 | -7 | -3 |
| Operational & Service Contracts | 6 | -5 | 7 | -6 | 2 |
| Concession Models | 4 | -7 | 6 | -6 | -3 |
| Outcome-Based Models (OBA) | 5 | -6 | 7 | -7 | -1 |
3. Historical Performance of PPP Models
Review data from past projects in similar settings to assign realistic weights. Successful projects in metros, for example, can highlight how effective technology transfer and private-sector expertise are in addressing water supply challenges, giving Strengths a higher weight.
Conversely, challenges in rural settings (e.g., cost recovery and political resistance) might make Weaknesses or Threats more prominent in rural areas, thus requiring higher weightage.
4. External Stakeholder Input
Minimize bias by consulting stakeholders such as government agencies, private sector partners, and communities. Weights should reflect a balance of views:
- Government Agencies: Focus on regulatory challenges, governance, and political risks.
- Private Sector: Insights into operational efficiency, profitability, and long-term sustainability.
- Community Groups: Emphasize affordability, accessibility, and fairness, particularly in rural areas and slums.
5. Sensitivity Analysis
Perform a sensitivity analysis to check the robustness of the assigned weights. By testing different weight scenarios (e.g., increasing the weight of Opportunities in Tier 2 cities vs. rural areas), you can see how shifts in assumptions affect outcomes. This ensures that no single factor disproportionately influences the evaluation.
Example Weighting for Different Settings
| Setting | Strengths | Weaknesses | Opportunities | Threats |
| Metros | 35% | 25% | 25% | 15% |
| Tier 2 Cities | 30% | 20% | 35% | 15% |
| Slums & Unauthorized Settlements | 25% | 40% | 20% | 15% |
| Small Towns | 30% | 25% | 30% | 15% |
| Regional Water Supply Schemes | 35% | 30% | 25% | 10% |
| Rural Areas | 20% | 35% | 25% | 20% |
Conclusion
By using objective, data-driven criteria and considering contextual relevance, one can assign weights to each SWOT factor in a systematic way that minimizes personal bias. Regularly involving stakeholders and using sensitivity analysis to test different weight scenarios will also ensure that the system remains fair and adaptable to different settings.
Example: SWOT-Based PPP Model Evaluation for South Delhi
Population Distribution in South Delhi:
| Area | Population (%) |
| Affluent Areas | 25% |
| Middle-Class Areas | 35% |
| Urban Slum Areas | 20% |
| Rural Areas | 20% |
| Total | 100% |
Step 1: Adjusted Weights for Strengths, Weaknesses, Opportunities, and Threats (SWOT)
| Area | Strengths (%) | Weaknesses (%) | Opportunities (%) | Threats (%) |
| Affluent Areas | 30% | 15% | 30% | 25% |
| Middle-Class Areas | 25% | 25% | 25% | 25% |
| Urban Slum Areas | 20% | 35% | 20% | 25% |
| Rural Areas | 25% | 25% | 25% | 25% |
| Total (for each SWOT) | 100% | 100% | 100% | 100% |
Step 2: Weighted SWOT Calculations for South Delhi
Strengths Calculation:
| Area | Population (%) | Strengths (%) | Weighted Strengths |
| Affluent Areas | 25% | 30% | 0.25 × 0.30 = 0.075 |
| Middle-Class Areas | 35% | 25% | 0.35 × 0.25 = 0.0875 |
| Urban Slum Areas | 20% | 20% | 0.20 × 0.20 = 0.04 |
| Rural Areas | 20% | 25% | 0.20 × 0.25 = 0.05 |
| Total Strengths | 0.2525 (25.25%) |
Weaknesses Calculation:
| Area | Population (%) | Weaknesses (%) | Weighted Weaknesses |
| Affluent Areas | 25% | 15% | 0.25 × 0.15 = 0.0375 |
| Middle-Class Areas | 35% | 25% | 0.35 × 0.25 = 0.0875 |
| Urban Slum Areas | 20% | 35% | 0.20 × 0.35 = 0.07 |
| Rural Areas | 20% | 25% | 0.20 × 0.25 = 0.05 |
| Total Weaknesses | 0.245 (24.5%) |
Opportunities Calculation:
| Area | Population (%) | Opportunities (%) | Weighted Opportunities |
| Affluent Areas | 25% | 30% | 0.25 × 0.30 = 0.075 |
| Middle-Class Areas | 35% | 25% | 0.35 × 0.25 = 0.0875 |
| Urban Slum Areas | 20% | 20% | 0.20 × 0.20 = 0.04 |
| Rural Areas | 20% | 25% | 0.20 × 0.25 = 0.05 |
| Total Opportunities | 0.2525 (25.25%) |
Threats Calculation:
| Area | Population (%) | Threats (%) | Weighted Threats |
| Affluent Areas | 25% | 25% | 0.25 × 0.25 = 0.0625 |
| Middle-Class Areas | 35% | 25% | 0.35 × 0.25 = 0.0875 |
| Urban Slum Areas | 20% | 25% | 0.20 × 0.25 = 0.05 |
| Rural Areas | 20% | 25% | 0.20 × 0.25 = 0.05 |
| Total Threats | 0.25 (25%) |
(Continued on next page)
Step 3: Final Weighted Scores for Each PPP Model
| PPP Model | Strengths Calculation | Weaknesses Calculation | Opportunities Calculation | Threats Calculation | Total Score |
| Build-Operate Models (DBO, BOT, BOO) | (7 × 25.25%) = 1.7675 | (-5 × 24.5%) = -1.225 | (6 × 25.25%) = 1.515 | (-4 × 25%) = -1.00 | 1.0575 |
| Operational & Service Contracts | (6 × 25.25%) = 1.515 | (-3 × 24.5%) = -0.735 | (8 × 25.25%) = 2.02 | (-3 × 25%) = -0.75 | 2.05 |
| Concession Models | (5 × 25.25%) = 1.2625 | (-6 × 24.5%) = -1.47 | (5 × 25.25%) = 1.2625 | (-5 × 25%) = -1.25 | -0.195 |
| Outcome-Based Models (OBA) | (4 × 25.25%) = 1.01 | (-4 × 24.5%) = -0.98 | (7 × 25.25%) = 1.7675 | (-4 × 25%) = -1.00 | 0.7975 |
Step 4: Conclusion
- Operational & Service Contracts is the most suitable PPP model for South Delhi, with a final score of 2.05.
- Build-Operate Models score 1.0575, making them moderately viable.
- Outcome-Based Models (OBA) score 0.7975, making them a less preferred option.
- Concession Models have a negative score (-0.195), indicating they are the least favourable.
Conclusion:
The quantitative SWOT-based Decision Support System (DSS) developed in this analysis offers a structured and objective framework for evaluating PPP models across diverse water supply settings in India. By systematically assigning weights to Strengths, Weaknesses, Opportunities, and Threats, this approach ensures that regional socio-economic, political, and infrastructure complexities are factored into decision-making.
The results demonstrate that Operational & Service Contracts consistently score the highest across most settings, including metros, Tier-2 cities, slums, and rural areas. This model offers flexibility, cost-effectiveness, and scalability, making it the most viable option for addressing the varying needs of these regions. Build-Operate Models, while feasible in certain urban contexts, face significant affordability challenges in rural and low-income areas, reducing their overall effectiveness. Concession Models and Outcome-Based Models (OBA), with lower scores across the board, face greater risks related to cost recovery, political resistance, and regulatory weaknesses, making them less attractive for widespread implementation.
The DSS provides a transparent, replicable tool for policymakers and private sector participants to objectively assess the feasibility of different PPP models. As more data becomes available and the socio-political landscape evolves, this system can be refined and adjusted to ensure its continued relevance. By leveraging such data-driven approaches, India can make informed decisions that balance public interests with the efficiency gains of private sector involvement in water delivery.
This model is open to further adjustments based on ongoing stakeholder inputs, technological advances, and region-specific performance data, ensuring it remains an adaptable and robust tool for future applications.
Inspire Positivity 