Conference Agenda

This paper investigates the effect of rainfall shocks on the allocation of household consumption among children, women, and men in Malawi. The identification relies on the spatial-temporal variation in the occurrence of rainfall shocks in four agricultural growing seasons between 2010 and 2019. I estimate a collective model of household to retrieve individual resource shares and their determinants. Results show that a drought in the growing season is likely to induce the redistribution of household resources from women and children towards men. Welfare analyses based on the comparison of individual consumption and poverty rates show that women tend to bear the burden of the shock within the household. The negative effect of a drought on women’s resource shares is more pronounced in areas where men are more actively involved in income-generating and off-farm activities than women after a drought. This suggests that the drought-induced redistribution of resources within household is likely motivated by ‘life-boat ethics’, that is, nourishing the members with higher marginal productivity and potential to bring cash income to the household.

Climate change has a significant impact on agriculture and repeatedly diminishes crop yields, with extreme weather events such as droughts, heavy rain, and heatwaves being recognized as major drivers of crop yield losses. In Germany, the agricultural sector experienced severe crop yield reductions due to unprecedented heat and drought in recent years, e.g. in 2003 and 2018, which led to significant economic damages. Given the increasing frequency and intensity of such extreme weather events, it is crucial to quantify the related future yield damages on different crops to inform prospective climate change adaptation planning.

In this study, we present a statistical crop yield modelling approach to project the yield changes under climate change for 8 major field crops in Germany, based on a diverse range of extreme weather events. To select the most relevant variables for prediction, we apply the least absolute shrinkage and selection operator (LASSO) within a regression framework. To train the crop yield models, we utilize district-level data on crop yields across Germany, along with meteorological data from the German Weather Service and soil moisture data from the mesoscale Hydrologic Model (mHM). For projecting the changes in crop yields for the near future (2030-60) and far future (2070-99), we utilize a bias-adjusted and spatially disaggregated EURO-CORDEX ensemble containing in total 88 simulations for RCP2.6, RCP4.5, and RCP8.5.

The LASSO models select on average two-thirds of the variables for extreme weather, indicating that a diverse range of extremes needs to be considered for yield prediction, also because of potentially compounding effects. In the training data, we find a pronounced negative impact of summer drought on the majority of field crops. However, for future projections, this effect is superseded by increasing heat impacts. This especially affects potatoes and silage maize in the far future (2070-98), while winter crops like rapeseed and winter barley are less affected. We conclude that the LASSO is a useful tool for projecting changes in crop yields of multiple crops related to a broad range of extreme weather events, which is imperative to estimate potential economic losses and to inform agricultural adaptation. However, we base our analysis on purely empirical relationships, highlighting the importance of validating these findings through the lenses of biophysical yield models.

Rural electrification initiatives involving the dissemination of solar home systems (SHS) are regarded as an important puzzle piece in the push towards the United Nations Sustainable Development Goal of universal electrification. Yet, the evidence on the socioeconomic impact of such projects is scarce with mixed results, while their environmental sustainability implications are mostly investigated with simulation studies. The study at hand studies the project’s long-term implications regarding both of these dimensions. Our analysis is based on a survey of n = 1,206 households in rural Sindh, Pakistan of which half were provided with an SHS during a large-scale development initiative a decade earlier. We employ propensity score matching with overlap weighting to investigate the socioeconomic impact of the SHS provision. The project’s ecological implications are proxied by an analysis of SHS energy payback time and the results of a survey experiment on the hypothetical discrete choice between SHS characterized by different sustainability attributes. We find evidence for an impact on socioeconomic indicators such as lighting and study hours. However, these effects depend heavily on whether households were able to maintain the SHS from project implementation until the time of the household survey. As only one-third of households in our study sample were able to do so, this also hampers the electrification project’s overall implications regarding ecological sustainability.

Cost risk analysis (CRA) continues emerging as a noticed decision-analytic framework within climate economics. It introduces the construct of a climate target into an expected utility-based structure and thereby avoids the dynamic inconsistencies of cost effectiveness analyses under uncertainty (i.e. of chance constraint programming). We present three innovations on CRA. They might complete the set of assumptions necessary for CRA as a ‘bridging technology’, before cost benefit analyses can more unequivocally be applied, in view of a currently potentially underdetermined global warming impact function. (i) We modify CRA such that risks below the climate target are taken care of, while still reflecting the specific choice of the target’s numerical value. (ii) This enables us to provide a consistent mechanism of including matured impact modelling components into a further extended CRA, such in the long run, CRA might converge towards cost benefit analysis. (iii) We provide an updating mechanism for including revisions of the science base of the climate target. We conclude from a thereby extended CRA, that a potential lowering of the temperature maxima the 2° target was based on, as suggested by latest paleo records (Westerhold et al. 2020), would not significantly change the temperature target. Hence, the joint science and economics base behind the Paris agreement could be argued to be still intact.