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This study is a product of the Africa Infrastructure Country Diagnostic (AICD), a project designed to expand the world's knowledge of physical infrastructure in Africa. The AICD provides a baseline against which future improvements in infrastructure services can be measured, making it possible to monitor the results achieved from donor support. It also offers a more solid empirical foundation for prioritizing investments and designing policy reforms in the infrastructure sectors in Africa. The book draws upon a number of background papers that were prepared by World Bank staff and consultants, under the auspices of the AICD. The main findings were synthesized in a flagship report titled Africa's infrastructure: A time for transformation, published in November 2009. Meant for policy makers, that report necessarily focused on the high-level conclusions. It attracted widespread media coverage feeding directly into discussions at the 2009 African union commission heads of state summit on infrastructure.

Energy production using hydropower has a 150-year history in Norway. High mountains, lots of rain, and a well-developed technology laid the foundation for low and stable electricity prices. The Norwegian electricity market is unique and different from any other country. Nearly all electricity produced (98.3 percent) comes from renewable energy sources and 75 percent of the energy used for heating is electricity. From autumn 2020, major changes have been observed in the electricity market in Norway. In 2021, Norway opened two transmission cables, one to Germany and one to England. Both cables have a capacity of 1400 MW. The average price per MWh was NOK 263 in southern Norway in the period 2013–2020, which more than quadrupled to NOK 1192 per MWh in the period 2021–2023. We have investigated how the market reacted to the large price increase. We found that price elasticity is low even when the price is very high. It is the temperature that controls the consumption. When it is cold—below zero degrees Celcius—the temperature elasticity is close to zero; the temperature elasticity is not constant. When the temperature is above zero, the temperature elasticity is about −0.7. Price variations or changes in wind speed only lead to minor adjustments in electricity consumption. It is the variations in temperature that result in the observable fluctuations in electricity consumption. Since Norway exports electricity to Sweden, Denmark, Finland, Germany, the Netherlands, and England, knowledge of the Norwegian electricity market is relevant for many market participants. The Norwegian electricity market differs from those in other countries. Therefore, there is a risk that conclusions drawn about the Norwegian electricity market based on research conducted in other countries may be incorrect or inaccurate. Our contribution with this case study is to deepen the knowledge of how the electricity market in Norway operates.

On 11 March 2020, the WHO declared the COVID-19 epidemic to be a global pandemic. This was a consequence of the rapid increase in the number of people with positive test results, the increase in deaths due to COVID-19, and the lack of pharmaceutical drugs. Governments introduced national lockdowns, which have impacted both energy consumption and economies. The purpose of this paper is to answer the following question: do COVID-19 lockdowns affect the business cycle? We used the cycle clock approach to assess the magnitude of decrease in electricity consumption in the three waves of the epidemic, namely, April 2020, November 2021, and April 2021. Additionally, we checked the relation between energy consumption and GDP by means of spectral analysis. Results for selected 28 European countries confirm an impact of the introduced non-pharmaceutical interventions on both energy consumption and business cycle. The reduction of restrictions in subsequent pandemic waves increased electricity consumption, which suggests movement out of the economic recession.

Global warming leads to the problem of climate adaptability, which makes residents’ electricity consumption behavior more sensitive to temperature. Understanding the shape of the temperature–electricity consumption response curve helps plan power investment and production and facilitates a green and low-carbon transformation of the power system. Using data regarding electricity consumption in nearly 20,000 households from seven cities in Anhui Province, China, from 2016 to 2017, this study examined the response of residential electricity consumption to temperature. The results show that there is a positive effect of the heating degree day (HDD) and cooling degree day (CDD) on residential electricity consumption. In particular, under the possible influence of the electricity price and weather factor, the electricity-temperature response curve has a “V”-shape when the average temperature is over 30 °C, and an extra day above 34 °C will increase monthly residential electricity consumption by 2.70%. The heterogeneity analysis shows that the temperature and electricity response curve have strong fluctuations under the time-of-use (TOU) pricing policy change. This implies that the price policy helps regulate the power consumption temperature response curve and thus impacts the power load.

Information and communication technologies (ICTs) have been a remarkable success in Africa. Across the continent, the availability and quality of service have gone up and the cost has gone down. In just 10 years dating from the end of the 1990s mobile network coverage rose from 16 percent to 90 percent of the urban population; by 2009, rural coverage stood at just under 50 percent of the population. Although the performance of Africa's mobile networks over the past decade has been remarkable, the telecommunications sector in the rest of the world has also evolved rapidly. Many countries now regard broadband Internet as central to their long-term economic development strategies, and many companies realize that the use of ICT is the key to maintaining profitability. This book is about that challenge and others. Chapters two and three describe the recent history of the telecommunications market in Africa; they cover such issues as prices, access, the performance of the networks, and the regulatory reforms that have triggered much of the investment. This part of the book compares network performance across the region and tries to explain why some countries have moved so much more quickly than others in providing affordable telecommunications services. Chapter four explores the financial side of the telecommunications revolution in Africa and details how the massive investments have been financed and which companies have most influenced the sector. Chapter five deals with the future of the sector. The final chapter synthesizes the main chapters of the book and presents policy recommendations intended to drive the sector forward.

When considering the electrification of a particular region in developing country, the electricity consumption in that region must be estimated. In sub-Saharan Africa, which is one of the areas with the lowest electrification rates in the world, the villages of minority groups are scattered over a vast area of land, so electrification using distributed generators is being actively studied. Specifically, constructing a microgrid or introducing a solar system to each household is being considered. In this case, the electricity consumption of each area needs to be estimated, then a system with enough capacity could be introduced. In this study, we propose a household income electricity consumption model to estimate the electricity consumption of a specific area. We first estimate the electricity consumption of each household based on income and the electricity consumption of a specific area can be derived by adding up them in that area. Through a case study in Tanzania, electricity consumption derived using this model was compared with electricity consumption published by TANESCO, and the validity of the model was verified. We forecasted the electricity consumption in each region using the household income electricity consumption model, and the average forecast accuracy was 74%. The accuracy was 87% when the electricity consumption in Tanzania mainland was forecasted by adding the predicted values.

Existing studies have shown that climate change has important implications for residential electricity consumption, yet how responses to climate vary between rural and urban residents, and more importantly, the roles of electricity pricing regimes in determining such responses remain largely unknown. In this paper, we explore these issues using monthly data in Anhui province in China. Our results suggest that on average rural residents are more sensitive to cooling degree days (CDD) than urban counterparts (0.19% vs 0.08% increase in electricity consumption per unit increase in CDD). Additionally, households who adopt the time of use (TOU) pricing regime tend to be less responsive to temperatures than households choosing tiered pricing regimes (TPHE). Substantial increases in electricity demand induced by climate change are expected in the future. With the pessimistic RCP8.5 scenario, our results suggest an increase of 35.5% and 77.1% in electricity demand respectively for the urban and rural residents in the 2080s relative to 2017.

With the rapid development of society and economy, the growth of electricity consumption has become one of the important indicators to measure the level of regional economic development. This paper utilizes NPP-VIIRS nighttime light remote sensing data to model electricity consumption in parts of southern China. Four predictive models were initially selected for evaluation: LR, SVR, MLP, and GBRT. The accuracy of each model was assessed by comparing real power consumption with simulated values. Based on this evaluation, the GBRT model was identified as the most effective and was selected to establish a comprehensive model of electricity consumption. Using the GBRT model, this paper analyzes electricity consumption in the study area across different spatial scales from 2013 to 2022, demonstrating the distribution characteristics of electricity consumption from the pixel level to the city scale and revealing the close relationship between electricity consumption and regional economic development. Additionally, this paper examines trends in electricity consumption across various temporal scales, providing a scientific basis for the optimal allocation of energy and the effective distribution of power resources in the study area. This analysis is of great significance for promoting balanced economic development between regions and enhancing energy efficiency.

To cope with the variability of electricity consumption patterns, this study proposes an online rolling optimization-based energy efficiency management strategy for smart homes, which considers user preferences on energy saving and electricity comfort. A weight parameter is used to balance these two objectives and users can set them according to their own electricity preferences. The strategy employs predictive models based on historical data and future inputs to forecast system outputs, and applies feedback correction to compensate prediction errors. In order to better express the power consumption satisfaction of users, two measures of user satisfaction are introduced: utility comfort and temperature comfort. Finally, the simulation result shows that the proposed method achieves an average energy reduction rate of 13.97%, which demonstrates that our strategy can achieve significant reductions in power consumption while enhancing user satisfaction.

A high-speed urban expansion in China over the past two decades has been accompanied by a great leap forward for energy consumption. However, such a significant socio-economic transition may increase the potential risk of energy inequality, which deserves special attention. Using China’s provincial panel data covering the periods of 1997–2020, this paper mainly studies the impact of urbanization on urban-rural electricity consumption inequality with a modified STRIPAT model. The results of the Generalized Method of Moments (GMM) estimation show that there is a significant U-shaped relationship between urbanization and urban-rural electricity consumption inequality. The estimated short-run turning point arrives at the urbanization level of around 63.54% and 61.18% for the long-run estimates. We further carry out a regional heterogeneity analysis and then have two interesting findings: firstly, the colder northern region’s turning point (70.95%) arrives later than the south (57.69%). Secondly, the baseline U-shaped relationship remains for developed eastern regions and the estimated turning point is 57.91%, while for the undeveloped midwestern regions, the relationship is not nonlinear but linearly negative. As an extension, we lastly explore the mechanism underlying the U-shaped relationship, and find that the interaction of urbanization’s scale and efficiency effect determines the U-shaped relationship. Our findings remind policymakers that, to narrow the urban-rural development gap, the future preference of energy policy should be dynamically adaptive to varied regions and development stages.