Conference Agenda

We study household electricity demand responses to hourly price variation under

real-time pricing using Finnish smart-meter data from 2019–2023. The analysis revisits

a widely used wind-based instrumental-variable design and shows that, in residential

settings with electric heating, local wind conditions can directly affect electricity

demand through heating-related channels, violating the exclusion restriction.

Explicitly controlling for local wind speed substantially revises elasticity estimates. In

normal market conditions, estimated household price responses are close to zero. By

contrast, during the 2022 energy crisis, households exposed to real-time pricing respond

significantly: an increase of 10 euro-cents per kWh in the retail electricity price

reduces consumption by about four percent. The direct effect of wind speed on demand

is economically meaningful and comparable in magnitude to large retail price

shocks observed during the crisis. Translating elasticities into bill impacts shows that

incentives for short-run load shifting are minimal in calm periods but become nonnegligible

when prices and volatility are exceptionally high.

Could energy transition policies inadvertently lead to adverse effects on households while pursuing sustainable development goals? Using data from the China Household Finance Survey (CHFS), this paper examines the impact of an energy transition policy in China, the New Energy Demonstration City Pilot Program, on household income, paying attention to vulnerable groups particularly. The results show that the policy lead to an average decline of 6.58% in total household income and a 5.04% reduction in income for the head of household. Specifically, the policy decreased individual wage income, household property income, and household transfer income. The latter two are the main contributors to the overall decline in total household income. Mechanism analysis suggests that employment structure adjustments, rising energy costs, and inadequate compensation mechanisms are the key transmission channels. Heterogeneity analysis demonstrates that energy transition has a stronger negative impact on the incomes of low-income households, low-asset families, employed individuals, and those with lower levels of education. Welfare analysis further reveals that the policy has a significantly stronger inhibitory effect on developmental consumption than on subsistence consumption, resulting in long-term welfare losses for households.

How do subnational tax collection efforts respond to windfall revenues from natural

resource royalties? Conversely, what are the fiscal implications when these revenues de-

cline? This paper investigates the effects of Colombia’s 2012 reform, which substantially

restructured the allocation of mining royalties among municipalities. By analyzing this

legal shift, we assess changes in municipal tax collection behavior. Municipalities that

experienced a reduction in royalties intensified their tax collection efforts; however,

these efforts did not fully offset the revenue losses. In contrast, municipalities that

benefited from increased royalties reduced their tax collection activities, resulting in

no net change in overall municipal income. These findings contribute to the literature

on the resource curse by offering a subnational perspective on the fiscal dynamics of

resource windfalls.

This study examines the impact of “Enudge,” an artificial intelligence (AI) energy management system (EMS), on electricity consumption in the retail sector. As retail installations increasingly contribute to nonindustrial CO₂ emissions, conventional EMSs frequently fail to manage the complex and variable energy demands in these settings. By leveraging a difference-in-differences framework on store-level data from over 1,700 retail stores in Japan between November 2018 and December 2023, this study finds that installation of AI EMS-Enudge reduces electricity consumption by an average of 1.9%. However, this reduction effect declines over time, with electricity savings diminishing within five to ten months. This decay effect is consistent with the decrease in user interaction with the recommendations provided by AI, suggesting that user engagement may play a crucial role in reducing electricity consumption. Heterogeneity analyses reveal that the system’s performance varies across retail establishments and seasonal contexts. Moreover, a cost-benefit analysis aimed at exploring break-even tariffs and implied abatement costs highlights that the installation of an AI EMS can contribute to cost savings, especially under high tariffs and higher-carbon grids.

This paper examines whether linking CEO compensation to environmental performance leads to measurable improvements in firms’ greenhouse gas emissions. Using global firm-level data from 2015–2024, we exploit staggered adoption and regulatory-driven variation in the introduction of environmental bonus components in CEO pay to track changes in Scope 1 emissions. Across all specifications, we find no statistically significant effect of introducing environmental incentives on direct emissions. To understand this null result, we analyze the structure of these incentives and show that environmental bonus components are typically small relative to total compensation and rely heavily on qualitative, non-verifiable targets. Combined with limited technical environmental expertise at the board level, these design features weaken the potential for incentives to influence operational decisions. Our findings suggest that current ESG-linked compensation schemes, particularly among U.S. firms facing regulatory-induced adoption, function largely as symbolic commitments rather than effective drivers of decarbonization, and highlight the need for more rigorous, quantitatively grounded incentive structures to align executive pay with climate goals.

Climate change is making extreme heat more frequent, and coastal load centers increasingly rely on imported power to meet cooling demand. Using a county–day panel for Zhejiang and Jiangsu linked to temperature, rainfall-deviation, and biweekly drought index (Standardized Precipitation and Evapotranspiration Index, SPEI) measures for the inland, power-supplying provinces, we test whether electricity use on very hot days is fully demand-driven. We first estimate a flexible temperature–electricity curve and confirm a steep rise in consumption above 28°C, especially above 32°C. We then show that this expected surge is noticeably smaller when the exporting provinces are themselves hot and dry: upstream climate stress weakens the local heat response even though local temperatures are high. This attenuation accounts for up to 50% of the local heat-induced electricity increase. This pattern is robust to alternative heat indicators, to 3–60 day rainfall deficits, and to SPEI weighted by upstream counties.

Evidence on local thermal generation indicates that coastal provinces do ramp local supply, but not enough to remove the constraint. Thus the insurance value of long-distance (UHV) transmission is climate-dependent, and heat-wave planning must allow for simultaneous inland scarcity.

I examine the optimal subsidy for renewable electricity in the absence of an optimal carbon tax. The current most common form of renewable subsidization, the fixed feed-in premium, provides a constant additive markup over wholesale prices. In contrast, the optimal subsidy encourages investment in the most socially valuable renewable production through a multiplicative markup over wholesale prices. In the baseline model, the optimal markup is easily calculable by policymakers, and the optimality of subsidies which increase with wholesale electricity price is robust to a wide range of extensions of the baseline model. Furthermore, I show that the optimal subsidy is preferred by risk-averse investors to a fixed feed-in premium.