Climate change, labour availability and the future of gender inequality in South Africa

Women in developing countries are more exposed to the adverse effects of climate change. We develop a structural model to study the long-term impacts of climate and socioeconomic changes on labour supply and the pay gap between male/female and high-skilled/low-skilled labour. We calibrate our model with empirical evidence on the impacts of increasing temperatures on labour availability in two general economic sectors with high and low exposure to rising temperatures. Using five waves of nationally representative micro-survey data in South Africa from 2008 to 2017, we find that while high-skilled labour availability is insensitive to climate change, higher temperatures have a negative impact on working hours of low-skilled labour specially among women in the high-exposure sector. We incorporate these findings in an overlapping generations (OLG) model to show that climate-induced reduction in labour availability increases the relative wages of low-skilled female labour and reduces the wage gap between male and female labour in the high-exposure sector, and between high-skilled and low-skilled female labour, in general. Considering climate change damages both on sectoral productivity and on labour availability, we project that by the end of the century, the output per adult will drop by about 11 percentage points under a severe climate scenario. This calls for more targeted adaptation policies that build on the potential benefits of climate change in reducing gender inequality and empowering women to take up more active roles in designing and implementing such policies at the local level

Mosquitoes and Potatoes: How Local Climatic Conditions Impede Development

The historical diffusion of the potato in the Old World serves as an example of the contribution of technological innovations to socio-economic growth and development (Nunn and Qian in Q J Econ 126(2):593–650, 2011). Climate-related diseases, on the other hand, might offset some of these benefits. Here we examine the long-term impact of malaria on the potato-driven growth of the population and urbanization in the Old World during the 18th and 19th centuries. We exploit local variations in environmental suitability both for potato and for malaria transmission to estimate and compare the impact of potato cultivation on population and urbanization in highly endemic to non-endemic areas at a high level of spatial disaggregation. We show that local climate conditions ideal for malaria transmission counteracted the potential benefits of introducing the potato to the Old World, which are conversely found to be strong and positive in non-endemic regions. These results highlight the interplay between technological change, public health, and development outcomes

Inequality and growth impacts of climate change—insights from South Africa

The impact of climate change on economic growth has been the subject of numerous studies in recent years, with macro-econometric analyses estimating the effect of rising temperatures on gross domestic product (GDP) growth rates at the country-level. However, the distributional impact of warming on inequality and poverty at the micro-level remains relatively unexplored. In this paper, we investigate the relationship between temperature and inequality in South Africa at the national and sub-national level. Our analysis reveals a significant ∪-shaped relationship between temperature and inequality indices, with inequality lowest at moderate temperatures (11 ◦C–18 ◦C) and increasing sharply as temperatures increase. We find that the optimal temperatures are lower for inequality measures than for income levels. This indicates that substantial increases in inequality are expected at higher temperatures compared to growth impacts. This effect is particularly noticeable for the poorer segments of the population, whose productivity and wages decline as temperatures increase, while the impact on the richer segments is less significant due to their greater adaptive capacity. In terms of mechanisms, we find that agricultural households are more likely to experience an increase in inequality due to warming. Our findings suggest that global warming has two adverse effects on hot countries: reducing average growth and increasing inequality. We compare the outcomes of the moderate RCP6.0 scenario to a reference scenario without warming and find that by the end of the century, the Gini coefficient in South Africa is expected to increase by 3–6 points, resulting in a potential welfare loss of approximately 50% when combined with the impact of warming on GDP (which alone can reach up to 43% by 2100 in South Africa). Our findings highlight the importance of investigating the distributional effects of climate change at the micro-level, particularly in low- or middle-income countries where vulnerable populations are more susceptible to its impacts

Prioritizing COVID-19 vaccine allocation in resource poor settings: Towards an Artificial Intelligence-enabled and Geospatial-assisted decision support framework

Objectives: To propose a novel framework for COVID-19 vaccine allocation based on three components of Vulnerability, Vaccination, and Values (3Vs). Methods A combination of geospatial data analysis and artificial intelligence methods for evaluating vulnerability factors at the local level and allocate vaccines according to a dynamic mechanism for updating vulnerability and vaccine uptake. Results: A novel approach is introduced including (I) Vulnerability data collection (including country-specific data on demographic, socioeconomic, epidemiological, healthcare, and environmental factors), (II) Vaccination prioritization through estimation of a unique Vulnerability Index composed of a range of factors selected and weighed through an Artificial Intelligence (AIenabled) expert elicitation survey and scientific literature screening, and (III) Values consideration by identification of the most effective GIS assisted allocation of vaccines at the local level, considering context-specific constraints and objectives. Conclusions: We showcase the performance of the 3Vs strategy by comparing it to the actual vaccination rollout in Kenya. We show that under the current strategy, socially vulnerable individuals comprise only 45% of all vaccinated people in Kenya while if the 3Vs strategy was implemented, this group would be the first to receive vaccines

Prioritizing COVID-19 vaccine allocation in resource poor settings: towards an artificial intelligence-enabled and geospatial-assisted decision support framework

Objectives: To propose a novel framework for COVID-19 vaccine allocation based on three components of Vulnerability, Vaccination, and Values (3Vs). Methods: A combination of geospatial data analysis and artificial intelligence methods for evaluating vulnerability factors at the local level and allocate vaccines according to a dynamic mechanism for updating vulnerability and vaccine uptake. Results: A novel approach is introduced including (I) Vulnerability data collection (including country-specific data on demographic, socioeconomic, epidemiological, healthcare, and environmental factors), (II) Vaccination prioritization through estimation of a unique Vulnerability Index composed of a range of factors selected and weighed through an Artificial Intelligence (AI-enabled) expert elicitation survey and scientific literature screening, and (III) Values consideration by identification of the most effective GIS-assisted allocation of vaccines at the local level, considering context-specific constraints and objectives. Conclusions: We showcase the performance of the 3Vs strategy by comparing it to the actual vaccination rollout in Kenya. We show that under the current strategy, socially vulnerable individuals comprise only 45% of all vaccinated people in Kenya while if the 3Vs strategy was implemented, this group would be the first to receive vaccines

Specialty grand challenge: renaming our section to “Carbon Dioxide Removal”

Peer Reviewe

Prioritizing COVID-19 vaccine allocation in resource poor settings: Towards an Artificial Intelligence-enabled and Geospatial-assisted decision support framework

Objectives: To propose a novel framework for COVID-19 vaccine allocation based on three components of Vulnerability, Vaccination, and Values (3Vs). Methods A combination of geospatial data analysis and artificial intelligence methods for evaluating vulnerability factors at the local level and allocate vaccines according to a dynamic mechanism for updating vulnerability and vaccine uptake. Results: A novel approach is introduced including (I) Vulnerability data collection (including country-specific data on demographic, socioeconomic, epidemiological, healthcare, and environmental factors), (II) Vaccination prioritization through estimation of a unique Vulnerability Index composed of a range of factors selected and weighed through an Artificial Intelligence (AIenabled) expert elicitation survey and scientific literature screening, and (III) Values consideration by identification of the most effective GIS assisted allocation of vaccines at the local level, considering context-specific constraints and objectives. Conclusions: We showcase the performance of the 3Vs strategy by comparing it to the actual vaccination rollout in Kenya. We show that under the current strategy, socially vulnerable individuals comprise only 45% of all vaccinated people in Kenya while if the 3Vs strategy was implemented, this group would be the first to receive vaccines

Learning in integrated optimization models of climate change and economy

Integrated assessment models are powerful tools for providing insight into the interaction between the economy and climate change over a long time horizon. However, knowledge of climate parameters and their behavior under extreme circumstances of global warming is still an active area of research. In this thesis we incorporated the uncertainty in one of the key parameters of climate change, climate sensitivity, into an integrated assessment model and showed how this affects the choice of optimal policies and actions. We constructed a new, multi-step-ahead approximate dynamic programing (ADP) algorithm to study the effects of the stochastic nature of climate parameters. We considered the effect of stochastic extreme events in climate change (tipping points) with large economic loss. The risk of an extreme event drives tougher GHG reduction actions in the near term. On the other hand, the optimal policies in post-tipping point stages are similar to or below the deterministic optimal policies. Once the tipping point occurs, the ensuing optimal actions tend toward more moderate policies. Previous studies have shown the impacts of economic and climate shocks on the optimal abatement policies but did not address the correlation among uncertain parameters. With uncertain climate sensitivity, the risk of extreme events is linked to the variations in climate sensitivity distribution. We developed a novel Bayesian framework to endogenously interrelate the two stochastic parameters. The results in this case are clustered around the pre-tipping point optimal policies of the deterministic climate sensitivity model. Tougher actions are more frequent as there is more uncertainty in likelihood of extreme events in the near future. This affects the optimal policies in post-tipping point states as well, as they tend to utilize more conservative actions. As we proceed in time toward the future, the (binary) status of the climate will be observed and the prior distribution of the climate sensitivity parameter will be updated. The cost and climate tradeoffs of new technologies are key to decisions in climate policy. Here we focus on electricity generation industry and contrast the extremes in electricity generation choices: making choices on new generation facilities based on cost only and in the absence of any climate policy, versus making choices based on climate impacts only regardless of the generation costs. Taking the expected drop in cost as experience grows into account when selecting the portfolio of generation, on a pure cost-minimization basis, renewable technologies displace coal and natural gas within two decades even when climate damage is not considered in the choice of technologies. This is the natural gas as a bridge fuel scenario, and technology advancement to bring down the cost of renewables requires some commitment to renewables generation in the near term. Adopting the objective of minimizing climate damage, essentially moving immediately to low greenhouse gas generation technologies, results in faster cost reduction of new technologies and may result in different technologies becoming dominant in global electricity generation. Thus today’s choices for new electricity generation by individual countries and utilities have implications not only for their direct costs and the global climate, but also for the future costs and availability of emerging electricity generation options.Ph.D

soheilsh/GreenTransition: Green Transition V1.0.1

No description provided

Staying home saves lives, really!

When coronavirus disease (COVID-19) was spreading worldwide, many national and local governments started to impose socially restrictive measures to limit the spread of the virus. Such quarantine measures in different cities worldwide have brought a new trend in public safety improvement and crime reduction. Using daily crime reports in the U.S., this paper evaluates the immediate unintended effects of shelter-in-place orders on different crime categories using fine-grained spatial units (i.e., neighborhoods) rather than entire cities, states, or countries. Results for San Francisco suggest an immediate drop of between 10 and 20% points in the total number of crimes after one month from the introduction of the restrictions. In particular, we show that while theft, homicide, and traffic accidents have fallen sharply, domestic violence incidents and weapon possession offences were not affected by the lockdown. The results are robust to the inclusion of spatial and temporal dependence