Conference Agenda

While the natural resource curse has been extensively studied empirically, the theoretical mechanisms underlying this phenomenon, particularly the role of demographic behaviour through fertility decisions, remain underexplored. We develop an overlapping generations model where final output depends on hu-

man capital, physical capital, and exhaustible natural resources to examine how resource related income distribution affects long-term economic growth. Our key finding demonstrates that resource related transfers to young generations—commonly proposed as a solution—may paradoxically intensify the resource

curse by encouraging investment in child quantity rather than quality. However, when governments instead direct royalty income from natural resources towards public provision of human capital, it improves the growth of human capital and can mitigate the resource curse. By analyzing the long-run per capita growth, we study the positive impact of public provision of human capital, with numerical simulations

using standard parameter values we demonstrate the quantitative relevance of our findings. Our analysis shows that resource related transfers lead to resource curse, while using wealth from royalty tax for public provision of human capital generates positive effects on growth. These findings provide crucial policy insights for resource-rich developing nations, suggesting that the optimal usage of resource related income determines whether natural resource wealth becomes a curse or a blessing for long-term prosperity.

What is the optimal carbon tax in a data-driven economy? We develop a dynamic general-equilibrium model that embeds data-economy features into a macro-environmental framework to study how AI-intensive production alters emissions and welfare. In the model, AI services combine unpriced, non-rival data, generated as a by-product of consumption, with energy-intensive compute supplied under monopolistic competition. These two distortions, together with the carbon externality, shape the economy’s response to climate policy. A calibration to the United States shows that the decentralized equilibrium delivers lower welfare than a planner solution because households undervalue the data they generate. The planner tolerates higher pollution to exploit data-driven productivity gains, implying a near-zero Pigouvian tax. In the decentralized economy, a carbon tax reduces emissions but also raises compute costs and slows the diffusion of AI. In a preliminary calibration, we estimate that the welfare-maximizing long-run tax on data centers is on the order of USD 40 per ton of CO2. These results suggest that data–compute complementarity plays an important role in shaping both emission dynamics and the incidence of carbon taxation.

Rebound effects—the increase in energy use following improvements in energy efficiency—are often cited as a fundamental obstacle to technological solutions to climate change. In response, sufficiency—understood as voluntary, welfare-enhancing reductions in demand—is increasingly proposed as a complement to efficiency but remains largely absent from macroeconomic models. We introduce sufficiency into a directed technical change model with energy–capital complementarity by incorporating a labor–leisure trade-off. Interpreting higher leisure valuation as sufficiency, we examine how demand-side restraint interacts with endogenous innovation after an energy-efficiency shock. We find that transitory sufficiency reduces energy use only temporarily and does not prevent rebound, while permanent shifts toward leisure lower steady-state energy use and eliminate long-run backfire. However, sufficiency also weakens incentives for energy-saving innovation by reducing energy prices. Sufficiency curbs rebound only if it is persistent.

This study investigates whether the resource curse hypothesis can be extended to renewable energy by constructing and empirically testing a novel metric of renewable resource rent. Using a global panel dataset of 46 countries from 2010 to 2022, we define renewable resource rent as the difference between industrial electricity price and levelized cost of electricity (LCOE), multiplied by renewable electricity generation. This framework enables the first direct measurement of economic rents from solar and wind energy. Our analysis reveals that both solar and wind resource rents are positively associated with national economic growth, suggesting the presence of a “renewable resource blessing” rather than a curse. Notably, solar potential significantly amplifies the growth effect of solar rents, while wind potential shows a weaker and inconsistent moderating effect. These findings are robust across alternative model specification. By directly incorporating renewable rents and potentials into a macroeconomic growth model, this study offers a methodological innovation and contributes empirical clarity to the largely conceptual debate on the renewable resource curse. Contrary to prior theoretical expectations that emphasize the risks of a solar-related curse in developing economies, our results highlight conditions under which solar energy can foster sustainable growth. We conclude with policy recommendations focused on institutional quality, energy diversification, and equitable value-chain participation to ensure that renewable energy development leads to long-term economic benefits. This research advances the debate on energy and growth by offering a replicable, rent-based approach to analyzing the sustainability of the global energy transition.

Is human-caused climate change likely to trigger enduring economic decline in modern societies? We study whether changes in violent conflict and economic growth caused by warming could interact to degrade economic opportunities and trap African countries in poverty. We provide evidence of an emerging high-conflict and low-growth "poverty trap" equilibrium in Africa and describe theoretically how such an equilibrium could result from warming. We then combine historical data on temperature, growth, and conflict with projections from a large ensemble of global climate models to evaluate the risk that warming and subsequent conflict could push African economies into a regime of sustained negative economic growth. We find that the risk of such "economic collapse" is material in a moderate-to-high emissions scenario. We estimate that in Africa, a current "high" emissions trajectory (RCP 7.0) may increase the incidence of conflict 5.1 percentage points (95% CI 0.2-13.0) and increase the share of countries with net negative GDP growth this century by 12.2 percentage points (95% CI 2.0-27.5). We calculate that roughly 82% of this additional conflict risk and 14% of projected GDP losses are due to the interacting effects between these two outcomes, underscoring the importance of accounting for their indirect effects and feedbacks. Our findings suggest an unprecedented scale of emissions mitigation, economic policy innovation or institutional investment that would be required to contain the risk of catastrophic human impacts from climate change in many African countries.

Nature and the ecosystem services derived from it are essential for humanity. Due to low, zero, or even negative prices, the contribution of natural capital to economic production has long been disregarded. Differentiating between provisioning and maintenance and regulating services, this paper estimates the contribution of natural capital to historical growth using panel data with an instrumental variable approach and time series estimations. While the results for maintenance and regulating services are inconclusive, provisioning services have substantially contributed to economic growth with large heterogeneity across countries.

Climate change poses significant risks to the global labour force, with rising temperatures adversely affecting both worker productivity and health. Exposure to extreme heat increases the incidence of heat stress, morbidity, and mortality, thereby reducing labour supply and raising hospitalization costs. This study investigates the compounded effects of temperature extremes on labour productivity, healthcare costs, and the role of increased energy consumption as a form of autonomous adaptation. We integrate this framework into the WITCH Integrated Assessment Model to assess the long-term economic implications of these impacts,

quantifying new energy trends and adaptation costs as increased AC stocks across future climate scenarios.

We developed an hybrid approach in which impacts on energy (supply and demand), health and labour due to temperature extremes are explicitly disaggregated from the impacts on the other sectors, allowing

us to explicitly model different channels of impacts and relative adaptation feedback. Impact functions for energy demand are derived from an econometric study by Colelli and Sue Wing (2025), which estimates the response of energy demand by fuel type and sector on a global scale, due to projected CDD (24) HDD (18) temperature extremes, following the methodology established in Colelli et al. (2022). These are endogenously determined by the climate emulator in the model. Projected energy increase is also used to determine the level of AC stock as increased adaptation investments. The same temperature extremes are

used to determine the climate change shock on the labour/weighted total factor productivity (tfpy), considering only the effect on low exposure sectors which benefit from AC. In this way, a direct link is established to explicitly account for the protective effect of adaptation on labour productivity. This allows to run different scenarios with or without the energy feedback. In this way, the model endogenously determines fuel specific energy demand and production across different scenarios. Additionally, following Adelaide et al. (2022), the impact of extreme heat on morbidity is assessed through an analysis of increased emergency visits, outpatient visits, and hospitalisations linked to extreme heat events, reflecting anticipated regional

disparities in health outcomes. Projected reductions in heat-related hospital admissions are directly linked with the increased adoption of AC stock endogenously determined. Three scenarios evaluated: (1)Baseline

(no climate, no adaptation): In this scenario the output level is the one in which there is no additional impact of temperature on labour productivity, and accordingly, no additional energy shall be used for adaptation. (2) Climate impact scenario: This scenario accounts only for the impact of heat on productivity, without

new energy consumption, implying lower levels of output Y. Increased hospitalization costs are added as well. (3) Adaptation scenario: Both the additional energy consumption and Ac stocks and reduced labour productivity are included. The endogenous response should therefore imply additional energy consumption

respect to the baseline to reduce the impact on labour. Depending on the adaptation effectiveness, the final consumption should roughly match the one in the baseline, net of the increased costs for the additional AC stocks.

Results are robust in showing how the negative effect on labour significantly affect highly exposed countries, but also many countries in European region and the US, leading to significant reduction in the economic output. The energy use for adaptation is assessed with a specific detail on energy sources, allowing

to evaluate also the impact on mitigation pathways and emissions.

Our results indicate that climate-induced labour impacts can lead to considerable economic losses and rising healthcare expenditures even in a moderate climate change scenario. While increased energy use for adaptation—primarily through cooling—proves cost-effective, it only partially offsets the broader negative

consequences for welfare and productivity.