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1. The need for agricultural productive uses in the national electrification plan of sub-Saharan African countries—a call to action for Ethiopia

   https://doi.org/10.1088/2634-4505/ac611e  

   Van-Hein Sackey, Charles ; Nock, Destenie ( May 2022 , Environmental Research: Infrastructure and Sustainability)

   Access to electricity is a crucial aspect of sub-Saharan Africa’s path towards development. In light of the potential for electricity access to improve quality of life, the United Nations aims to achieve universal access to ‘clean, reliable, affordable and modern’ electricity as Goal 7 of its Sustainable Development Goals (SDG 7). As such, governments of sub-Saharan African (SSA) countries, such as Ethiopia, have developed national electrification plans to outline their pathway to universal access to electricity. In this paper, we identify why it is essential for the national electrification plans of SSA countries to prioritize electricity access for productive uses in its agricultural sector, using Ethiopia as a case study. Reviewing existing literature and using the authors’ research, we point out that there is 3.04 terawatt-hours of latent demand for small-scale pressurized cereal-crop irrigation alone in Ethiopia. Supplying this electricity demand for small-scale irrigation could lead to a reduction in the levelized cost of electricity of up to 95%. We conclude our paper by recommending the creation of a cross-sector national productive use commission that would be tasked with collecting and sharing relevant data from each sector and collaboratively creating a national productive use program that would ensure thatmore »Ethiopia reaps the full benefits and potential for wealth creation from access to electricity.

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2. Educational Assortative Mating in Sub-Saharan Africa: Compositional Changes and Implications for Household Wealth Inequality

   https://doi.org/10.1215/00703370-9000609  

   Pesando, Luca Maria ( February 2021 , Demography)

   Abstract Sub-Saharan Africa (SSA) is undergoing rapid transformations in the realm of union formation in tandem with significant educational expansion and rising labor force participation rates. Concurrently, the region remains the least developed and most unequal along multiple dimensions of human and social development. In spite of this unique scenario, never has the social stratification literature examined patterns and implications of educational assortative mating for inequality in SSA. Using 126 Demographic and Health Surveys from 39 SSA countries between 1986 and 2016, this study is the first to document changing patterns of educational assortative mating by marriage cohort, subregion, and household location of residence and relate them to prevailing sociological theories on mating and development. Results show that net of shifts in educational distributions, mating has increased over marriage cohorts in all subregions except for Southern Africa, with increases driven mostly by rural areas. Trends in rural areas align with the status attainment hypothesis, whereas trends in urban areas are consistent with the inverted U-curve framework and the increasing applicability of the general openness hypothesis. The inequality analysis conducted through a combination of variance decomposition and counterfactual approaches reveals that mating accounts for a nonnegligible share (3% to 12%) ofmore »the cohort-specific inequality in household wealth, yet changes in mating over time hardly move time trends in wealth inequality, which is in line with findings from high-income societies.« less
3. Mapping Open Data and Big Data to Address Climate Resilience of Urban Informal Settlements in Sub-Saharan Africa

   https://doi.org/10.3390/cli10120186  

   Banzhaf, Ellen ; Bulley, Henry N. ; Inkoom, Justice Nana ; Elze, Sebastian ( December 2022 , Climate)

   This perspective paper highlights the potentials, limitations, and combinations of openly available Earth observation (EO) data and big data in the context of environmental research in urban areas. The aim is to build the resilience of informal settlements to climate change impacts. In particular, it highlights the types, categories, spatial and temporal scales of publicly available big data. The benefits of publicly available big data become clear when looking at issues such as the development and quality of life in informal settlements within and around major African cities. Sub-Saharan African (SSA) cities are among the fastest growing urban areas in the world. However, they lack spatial information to guide urban planning towards climate-adapted cities and fair living conditions for disadvantaged residents who mostly reside in informal settlements. Therefore, this study collected key information on freely available data such as data on land cover, land use, and environmental hazards and pressures, demographic and socio-economic indicators for urban areas. They serve as a vital resource for success of many other related local studies, such as the transdisciplinary research project “DREAMS—Developing REsilient African cities and their urban environMent facing the provision of essential urban SDGs”. In the era of exponential growth of bigmore »data analytics, especially geospatial data, their utility in SSA is hampered by the disparate nature of these datasets due to the lack of a comprehensive overview of where and how to access them. This paper aims to provide transparency in this regard as well as a resource to access such datasets. Although the limitations of such big data are also discussed, their usefulness in assessing environmental hazards and human exposure, especially to climate change impacts, are emphasised.« less
4. Quantifying the electricity, CO ₂ emissions, and economic tradeoffs of precooling strategies for a single-family home in Southern California*

   https://doi.org/10.1088/2634-4505/ac5d60  

   Mayes, Stepp ; Sanders, Kelly ( May 2022 , Environmental Research: Infrastructure and Sustainability)

   High fractions of variable renewable electricity generation have challenged grid management within the balancing authority overseen by the California’s Independent System Operator (CAISO). In the early evening, solar resources tend to diminish as the system approaches peak demand, putting pressure on fast-responding, emissions-intensive natural gas generators. While residential precooling, a strategy intended to shift the timing of air-conditioning usage from peak-demand periods to cheaper off-peak periods, has been touted in the literature as being effective for reducing peak electricity usage and costs, we explore its impact on CO₂emissions in regional grids like CAISO that have large disparities in their daytime versus nighttime emissions intensities. Here we use EnergyPlus to simulate precooling in a typical U.S. single-family home in California climate zone 9 to quantify the impact of precooling on peak electricity usage, CO₂emissions, and residential utility costs. We find that replacing a constant-setpoint cooling schedule with a precooling schedule can reduce peak period electricity consumption by 57% and residential electricity costs by nearly 13%, while also reducing CO₂emissions by 3.5%. These results suggest the traditional benefits of precooling can be achieved with an additional benefit of reducing CO₂emissions in grids with high daytime renewable energy penetrations.
5. mRSC: Multidimensional Robust Synthetic Control

   https://doi.org/10.1145/3309697.3331507  

   Amjad, Muhammad Jehangir ; Misra, Vishal ; Shah, Devavrat ; Shen, Dennis ( July 2019 , ACM SIGMETRICS)

   We propose an algorithm to impute and forecast a time series by transforming the observed time series into a matrix, utilizing matrix estimation to recover missing values and de-noise observed entries, and performing linear regression to make predictions. At the core of our analysis is a representation result, which states that for a large class of models, the transformed time series matrix is (approximately) low-rank. In effect, this generalizes the widely used Singular Spectrum Analysis (SSA) in the time series literature, and allows us to establish a rigorous link between time series analysis and matrix estimation. The key to establishing this link is constructing a Page matrix with non-overlapping entries rather than a Hankel matrix as is commonly done in the literature (e.g., SSA). This particular matrix structure allows us to provide finite sample analysis for imputation and prediction, and prove the asymptotic consistency of our method. Another salient feature of our algorithm is that it is model agnostic with respect to both the underlying time dynamics and the noise distribution in the observations. The noise agnostic property of our approach allows us to recover the latent states when only given access to noisy and partial observations a la amore »Hidden Markov Model; e.g., recovering the time-varying parameter of a Poisson process without knowing that the underlying process is Poisson. Furthermore, since our forecasting algorithm requires regression with noisy features, our approach suggests a matrix estimation based method-coupled with a novel, non-standard matrix estimation error metric-to solve the error-in-variable regression problem, which could be of interest in its own right. Through synthetic and real-world datasets, we demonstrate that our algorithm outperforms standard software packages (including R libraries) in the presence of missing data as well as high levels of noise.« less