Handling preference heterogeneity for river services' adaptation to climate change

Area of Science:

• Environmental Economics
• Climate Change Adaptation
• River Basin Management

Background:

• Climate projections for the Southern Mediterranean indicate a significant drought trend, threatening river ecosystem services, sectoral outputs, and household welfare.
• River systems are vulnerable to climate change, necessitating proactive adaptation strategies to maintain essential environmental services.

Purpose of the Study:

• To evaluate local residents' adaptation preferences and willingness to pay for sustaining services in the Piave River basin, Italy.
• To analyze the impact of different econometric approaches to heterogeneity on welfare estimates for environmental adaptation.

Main Methods:

• A Discrete Choice Experiment was employed to assess adaptation scenarios for Piave River services.
• Econometric analysis utilized Random Parameters Logit, Latent Class, and Covariance Heterogeneity models.
• Willingness to pay was estimated using compensating surplus for maintaining the river's current state.

Main Results:

• Respondents expressed a willingness to pay for adaptation plans, with an estimated monthly household contribution of 80€ to sustain the current state of the Piave River.
• Solely considering general heterogeneity in econometric models is insufficient, as distinct treatments yield significantly different welfare estimates.
• Approximating heterogeneity appropriately is vital for unbiased and accurate valuation of non-market environmental services.

Conclusions:

• Non-market values of environmental services are essential for formulating cost-effective adaptation measures in river systems facing climate change.
• Accurate assessment of heterogeneity in econometric models is critical for reliable welfare estimates and policy-making in environmental management.
• Local preferences and economic valuation are key components in developing resilient river basin management strategies under climate change scenarios.