Conference Agenda

The past three decades have witnessed a dramatic expansion of artisanal and small-scale gold mining (ASgM), transforming the economic and spatial opportunities of tens of millions of people. We show how this transformation has shaped urbanization in Sub-Saharan Africa since 1975. Our empirical strategy exploits plausibly exogenous variation in ASgM activity generated by the interaction between international gold-price shocks and local geological suitability for artisanal extraction, which we combine with new continent-wide data on urban population, nighttime lights, and household welfare. Although ASgM is commonly viewed as a rural activity, we find that ASgM exposure significantly accelerates urbanization, accounting for roughly five percent of total urban population growth. This expansion takes the form of extensive, decentralized urbanization: new towns emerge in remote, infrastructure-poor areas, while the growth of pre-existing towns and cities does not accelerate. Both new and existing urban entities exposed to ASgM exhibit lower living standards and limited industrial activity. Overall, ASgM contributes to a fragmented pattern of urbanization without structural transformation.

This paper examines the effects of state-level building codes on housing development

in wildfire-prone areas of California. Using a spatial regression discontinuity design

around regulatory boundaries, I find a significant decrease in housing development and

taxable property value in regulated areas. Next, I examine why state intervention is

necessary, given that climate adaptation is typically implemented by local governments.

I propose a conceptual model that illustrates how the perverse incentive to gain property tax revenue can lead to a distortion in the decision making of local governments. Empirically testing the model, I find that the effects of the state regulation are larger where local jurisdictions have a higher dependency on property tax revenue. The study results shed light more generally on the challenges of climate adaptation (e.g. managed retreat) when property tax incentives may create inefficient local government policies to correct housing market failures.

There is a critical gap in macro-economic modelling, namely the lack of representation of ecosystems and biodiversity. These provide services such as pollination, soil erosion and flood control, which contribute a significant proportion to global GDP. Their omission leads to inaccurate long-term socioeconomic projections, particularly under accelerating environmental change.

This study addresses this gap by explicitly integrating ecosystem services and biodiversity loss into long-term socioeconomic projections under climate change. Building on the European ecosystem accounting standards (SEEA-EA), we evaluate the impact of the decline in available pollination services across the EU using (1) observed trends in wild pollinator populations from European indexes as the EU Butterfly Index and (2) projected declines derived from state-of-the-art climate-driven species distribution models (SDMs) for key pollinator taxa (wild bees, hoverflies, butterflies, beetles). The ecological projections are spatially matched with crop cultivation areas from FAO Cropgrids and pollination dependency ratios for 103 european crops to estimate agricultural productivity losses at high spatial resolution under several climate scenarios (RCP2.6; 4.5; 6.0; 7.0 and 8.5) and socioeconomic pathways (SSP1; 2; 3; 5) up to 2070. These biophysical impacts are translated into second-order economic shocks using a regionalized version of the ICES Computable General Equilibrium (CGE) model (GTAP11), enabling assessment of macroeconomic consequences at NUTS2-level resolution. A conservation policy scenario is also evaluated, representing the implementation of main EU conservation policies available to date (Common Agricultural Policy (CAP), Nature Restoration Law, Natura2000 network). This scenario assumes stabilization and restoration of pollination potential to match 1990 levels in the absence of climate change, reflecting the anticipated effects of EU biodiversity policies through habitat restoration, pesticide reduction, and protected area expansion.

Results highlight the spatial heterogeneity of pollination decline and its disproportionate effects on pollinator-dependent crops across regions, revealing critical vulnerabilities in the EU agricultural systems. Although the aggregate impact on EU GDP remains modest, preliminary results indicate that economic impacts on the agricultural sector could reach up to –2.52% by 2080 relative to a no-climate-change scenario, with several regions experiencing losses exceeding –5%. Sectoral estimates show that numerous regions may face reductions of 9% to 22% of sectoral GDP in the case of Vegetables, fruit, nuts, Oil seeds and Other crops sectors, with effects propagating also to sectors not strictly dependent on pollination. Overall, excluding additional co-benefits such as flood control and soil-erosion mitigation, the conservation policy does not appear cost-efficient, as projected economic costs slightly outweigh the benefits associated with net increases in land productivity.

By stepping into the integration of biodiversity loss within climate-change macroeconomic impact assessment, this study delivers the first long-term evaluation of pollination impacts derived from a multi-model, multi-scenario SDM ensemble. The proposed framework provides a reproducible basis for embedding biodiversity dynamics into economic planning and for informing nature-restoration and climate-adaptation policies.

Households in the United States face compounded risks when high energy burdens intersect with heightened exposure to climate extremes. This study introduces a methodological framework to systematically identify and analyze these dual-vulnerability households. Using the 2020 Residential Energy Consumption Survey (RECS) linked with high-resolution NASA Daymet climate data, I spatially match households to localized measures of extreme climates like heatwaves and cold spells. Geospatial hotspot analysis reveals regions where economic and climatic risks converge, while Local Linear Forest models are employed to identify the primary drivers of household energy expenditures. The results show that climate-related variables, such as extreme temperature events and degree days, together with housing characteristics have a stronger influence on energy costs than income. Policy simulations of interventions, including appliance upgrades, weatherization, smart home technologies, and financial assistance programs, demonstrate heterogeneous effects across household types, with technology-based strategies delivering the largest reductions in energy expenditures. These findings highlight the importance of regionally tailored, climate-responsive policies that integrate technical upgrades with socioeconomic support to enhance resilience among households at the intersection of energy poverty and climate risk.