Conference Agenda

Climate change increases household energy burdens, disrupts electricity supply, and disproportionately harms vulnerable rural populations. However, little is known about how rural households adapt extreme weather shocks in electricity markets. Using fine-scale data on rural residential rooftop PV (RRPV) installations, electricity generation, and consumption form China, we find that extreme heat increases RRPV adoption under net metering, shifting rural households from net consumers to prosumers as a climate adaptation strategy, with storage enabling electricity autarky. This shift is driven by rising electricity costs and outage concerns, with stronger effects in regions with greater temperature fluctuations. We find no income-based differences in adoption rates, likely due to favorable PV loans and intergenerational transfer. Our findings highlight that RRPV can improve welfare and underscore the need to consider short-term behavioral responses in climate impact assessments and RRPV policy design.

The use of renewable energy sources, the energy crisis and the increased frequency and intensity of high heat events are changing the conditions under which European households consume energy services for their thermal comfort. Leveraging high-frequency residential electricity consumption data of over 10,000 households of one municipality in Northern Italy, between 2021 and 2022, we show that Photovoltaic (PV) adoption reduces electricity extraction from the grid, during high price and temperature events, enhancing energy security and affordability. Based on our estimated demand functions, we measure significant differences in the price-induced consumer surplus loss for households with and without PV, which amount to 133 and 300 euros, respectively, highlighting substantial private benefits from PV adoption during price fluctuations. Also, we evaluate the social benefits of PV adoption by using the global estimates of the Social Cost of Carbon (SCC) to monetize the value of avoided negative externalities due to GHGs emissions. The adoption of PV by an average household in Brescia spares up to 544 Kg of CO2 in a year, leading to a reduction of environmental damages for a value of 166 - 266 €/year.

Growing climate change impacts call for increasing efforts to adapt and reduce adverse consequences on society. However, adaptation is often costly for both governments and private citizens and it can itself drive additional impacts. A key example is the high energy-intensiveness of appliances for indoor air temperature regulation. In this paper, we empirically evaluate the temperature regulation effect of street green space (SGS) and its relevance for residential electricity demand. We exploit a monthly panel of household metered electricity demand data from n=129,524 households located in 2,181 municipalities distributed across Italy in the 2020-2022 period. Based on the empirical analysis, we find that SGS is an important regulating factor in the temperature-energy demand relation. Specifically, we find that SGS density has a strongly significant non-linear effect on the the impact of temperature of household electricity demand, with the strongest impact being a consumption reduction effect under hot temperatures. Such responses are heterogeneous, as they depend on the heat exposure level and the degree of urbanisation. We estimate that a policy aimed at increasing SGS to a minimum level of municipality-level mean GVI of 25 - a value currently at around the 75th percentile of the distribution in the municipalities covered by our analysis - would strongly contribute to counterbalancing the climate-change induced increase in residential electricity consumption. Such policy would reduce by more than two thirds the growth in the summer-months residential electricity consumption driven by climate change (under RCP 8.5 climate conditions around 2050), compared to the absence of such policy. Our results provide for the first time a range of empirical estimates for the role of SGS in the temperature-energy demand relation. Such evidence can be relevant both in relation the potential of energy demand reductions in supporting climate change mitigation efforts, and in the quest for sustainable, nature-based solutions for adaptation to climate impacts such as heat exposure.

This paper evaluates the impact of the DEPHY network, France's key pesticide reduction policy based on knowledge transmission through model farms, on farmer's pesticide usage. Using a causal framework inspired by the social learning literature, it finds that the DEPHY program reduced pesticide usage by approximately 10% between 2008 and 2021, with the effects concentrated in industrial farming systems such as cereal and mixed (cattle-crop) farms. Indirect transmission effects accounted for no more than 20% of the reduction, highlighting the limited role of social learning in this setting. Through an instrumental variable analysis, it shows that farmers who reduced pesticide use due to the program experienced increased revenue but also faced heightened variability, which could be an obstacle for the adoption of agro-ecological practices by risk-averse farmers. By leveraging fine, individual-level data for heterogeneity analysis, it provides actionable policy recommendations to improve the scheme in the future.