Group 03
Economic Reasoning Behind Households’ Income Level in Transitioning to Clean Energy
Dear Group 3,
Thank you for your report on household income and the clean energy transition. You did a good job motivating the topic via externalities, adoption gaps, and equity concerns. The potential for success is strong, especially if you narrow the scope to one technology and clarify whether your main lens is income constraints, information gaps, or their interaction. Tying your analysis more tightly to a simple household decision model will strengthen both the economic reasoning and policy relevance.
Next Steps
You are working toward both the presentation and the final proposal. Focus on economic reasoning supported by existing literature and descriptive evidence.
- Clarify your main angle
- Decide whether your core focus is:
- income and credit constraints,
- income and credit constraints,
- information and behavioral barriers, or
- information and behavioral barriers, or
- the interaction between the two.
- Decide whether your core focus is:
- Choose one focal technology
- For example: EVs, rooftop solar, heat pumps, or home energy-efficiency upgrades.
- Use this technology as your running example throughout the proposal.
- For example: EVs, rooftop solar, heat pumps, or home energy-efficiency upgrades.
- Recommend key empirical references
- Add at least one section that summarizes what we already know from empirical work:
- “Determinants of household adoption of clean energy with its rural–urban disparities in Bangladesh” (Scientific Reports, 2024)
- “Does renewable energy reduce energy intensity? A matter of income inequality” (Humanities and Social Sciences Communications, 2025)
- “Determinants of households’ investment in energy efficiency and renewables: evidence from the OECD survey on household environmental behaviour and attitudes” (Environmental Research Letters, 2015)
- Briefly connect these results to your own question about income and clean-energy adoption and to the idea that inequality can blunt the benefits of clean energy.
- Add at least one section that summarizes what we already know from empirical work:
- Use simple visual and descriptive evidence in your slides
- For your final presentation slides, I encourage you to show one or two figures or tables from these studies (or recreate a simplified version), such as:
- A figure or table of adoption rates by income or wealth group.
- A figure illustrating how the impact of renewable energy on energy intensity changes at different levels of income inequality.
- A figure or table of adoption rates by income or wealth group.
- Your task is to interpret what these patterns mean for equity and policy design.
- For your final presentation slides, I encourage you to show one or two figures or tables from these studies (or recreate a simplified version), such as:
- Develop a simple household decision framework
- Sketch a small household decision model (e.g., a net present value comparison of “stay with fossil” vs “adopt clean tech”) under different:
- income levels,
- discount rates,
- policy incentives (rebates, tax credits, low-interest loans).
- Use this framework to interpret why adoption differs across income groups.
- Sketch a small household decision model (e.g., a net present value comparison of “stay with fossil” vs “adopt clean tech”) under different:
- Add a short policy implications section
- Explain how your economic reasoning could inform the design of clean-energy support schemes across income groups (e.g., upfront rebates vs tax credits, on-bill financing, targeted programs for renters or low-income households).
Questions to Think About as You Refine Your Final Proposal
You do not need to answer all of these, but they may help you sharpen your story and policy recommendations.
1. Income Constraints & Economic Reasoning
- When a household considers your chosen technology (e.g., EV, rooftop solar, heat pump, or major efficiency upgrade), which part of the decision is hardest for low- and moderate-income households?
- High upfront cost?
- Limited access to affordable credit?
- Perceived risk/uncertainty about future energy savings or technology performance?
- How does income level affect the household’s discount rate or patience?
- For example, why might low-income households place more weight on short-run cash flow than on long-run energy savings?
- How do the Bangladesh and OECD studies reinforce the idea that income and wealth strongly predict adoption, even when technologies are socially beneficial? Which mechanisms from those papers seem most relevant for your chosen technology?
- How does the “renewable energy–energy intensity–income inequality” paper support the claim that inequality can reduce the effectiveness of clean energy in improving outcomes, and how might that translate into the household-level story you are telling?
2. Policy Design & Equity
- Suppose you design one flagship policy to help low- and middle-income households adopt your chosen technology without making the policy regressive.
- What features would it need in terms of:
- Timing of support (upfront rebate vs tax credit vs loan)?
- Targeting (income-tested, place-based, renters vs owners)?
- Complexity (paperwork, information requirements)?
- Timing of support (upfront rebate vs tax credit vs loan)?
- What features would it need in terms of:
- Looking at the evidence that higher-income and urban households adopt clean energy at much higher rates, how would you judge whether your policy actually narrows adoption gaps rather than just subsidizing households who would have adopted anyway?
- Are there complementary policies (e.g., building codes, landlord incentives, consumer protection in financing) that could help overcome structural barriers faced by lower-income households?
3. Behavioral & Information Barriers
- Where do bounded rationality and bounded willpower show up in these decisions?
- Examples: procrastinating on home upgrades, ignoring complex program details, distrust or confusion about new technologies, focusing only on upfront price and not lifetime cost.
- How might these behavioral factors interact with income constraints to reinforce existing inequalities?
- For example, are low-income households more exposed to misleading offers or more likely to drop out of complex application processes?
- Based on the empirical studies and your own reasoning, what kinds of behaviorally informed policy tweaks (simplified forms, default enrollment, trusted messengers, clear comparison tools) might make a meaningful difference for lower-income households?
Best,
Byeong-Hak