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Abstract In South Africa, the electricity mismatch of supply and demand has been of major concern. Additional to past problems, the 2008 electricity crisis made the solution crucial after its damaging consequences to the economy. The disagreement on the need and consequences of the continuous electricity price hikes worsens the situation. To contribute to the recent electricity debate, this paper proposes a time-varying price elasticity of demand for electricity; the sensitivity of electricity consumption to price fluctuations changes throughout the years. The main purpose of this study is the estimation of the price elasticity of electricity in South Africa during the period 1980–2005 by employing an advanced econometric technique, the Kalman filter. Apart from the decreasing effect of electricity prices to consumption (−71.8% in the 1990s and −94.5% in the 2000s in average), our results conclude to an important finding: the higher the prices (for example in the 1980s) the higher the sensitivity of consumers to price fluctuations. Thus, further increases of the electricity prices may lead to changes in the behaviour of electricity consumers, focusing their efforts on improving their efficiency levels by introducing demand-side management techniques or even turning to other sources of – cheaper – energy.

Abstract In developed countries, buildings are involved in almost 50% of total energy use and 30% of global green-house gas emissions. Buildings' operational energy is highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based building energy modeling, machine learning techniques can provide faster and higher accuracy estimates, given buildings' historic energy consumption data. Looking beyond individual building levels, forecasting buildings’ energy performance helps city and community managers have a better understanding of their future energy needs, and plan for satisfying them more efficiently. Focusing on an urban-scale, this study systematically reviews 70 journal articles, published in the field of building energy performance forecasting between 2015 and 2018. The recent literature have been categorized according to five criteria: 1. Learning Method, 2. Building Type, 3. Energy Type, 4. Input Data, and 5. Time-scale. The scarcity of building energy performance forecasting studies in urban-scale versus individual level is considerable. There is no study incorporating building functionality in terms of space functionality share percentages, nor assessing the effects of climate change on urban buildings energy performance using machine learning approaches and future weather scenarios. There is no optimal criteria combination for achieving the most accurate machine learning-based forecast, as there is no universal measure able to provide such global comparison. Accuracy levels are highly correlated with the characteristics of forecasting problems. The goal is to provide a comprehensive status of machine learning applications in urban building energy performance forecasting, during 2015–2018.

The Nigerian government is committed to sustaining rice production to meet national demand. Nevertheless, political tension and climate-induced stressors remain crucial constraints in achieving policy targets. This study examines whether climate change and political instability significantly threaten rice production in Nigeria. First, we employed nonparametric methods to estimate the country's rainfall and temperature trends between 1980Q1 and 2015Q4. Second, we employed the autoregressive distributed lag (ARDL) technique to examine the effects of climate change and political instability on rice production. The results show that while temperature has an increasing pattern, rainfall exhibits no significant trend. The findings from the ARDL estimate reveal that rice production responds negatively to temperature changes but is less sensitive to changes in rainfall. In addition, political instability adversely affects rice production in Nigeria. We argue that Nigeria's slow growth in rice production can be traced back to the impact of climate change and political tension in rice farming areas. As a result, reducing the overall degree of conflict to ensure political stability is critical to boosting the country's self-sufficiency in rice production. We also recommend that farmers be supported and trained to adopt improved rice varieties less prone to extreme climate events while supporting them with irrigation facilities to facilitate rice production.

• Premise of the study: Climate change is associated with phenological shifts in an increasing number of organisms worldwide. However, accurate estimates of these shifts are dependent on long‐term data sets that include phenological observations from before annual average temperatures began to rise.• Methods: We compared the first flowering times of native prairie plants between 2007 and 2010 with historical data recorded by O. A. Stevens from 1910 to 1961. By merging climate variable data from the same time period, it also was possible to correlate first flowering dates with associated climate variables.• Key results: Over the past 100 years, spring temperatures in the Red River Valley near Fargo, North Dakota, USA, have increased, and growing seasons have lengthened significantly. Seventy‐five percent of the 178 species observed by Stevens had flowering times that were sensitive to at least one variable related to temperature or precipitation. Over the past 4 yr, 5% to 17% of the species observed have significantly shifted their first flowering time either earlier or later relative to the previous century.• Conclusions: The results of this study indicate that as spring temperatures in the northern Great Plains have increased and the growing season has lengthened, some spring flowering species have advanced their first flowering time, some fall species have delayed their first flowering, and some species have not changed. Given the importance of flowering timing for reproductive success, the changing climate in the Great Plains is expected to have long‐term ecological and evolutionary consequences for native plant species.

Abstract The 2013/2017 Nankai Trough (Japan) and 2017 Shenhu Area (China) offshore methane hydrate production tests showed the world the possibility and feasibility of the oceanic methane hydrate production by depressurization. However, the relatively low gas production rate still remained as one of the critical bottlenecks for the economical utilization. This study chose the Nankai Trough as a target area, and aimed at the gas recovery enhancement from the methane hydrate reservoir using vertical wells. A traditional single-vertical-well system and a new dual-vertical-well system were proposed, and special production strategies of the aggressive depressurization and permeability improvement were applied to these two systems for the effectiveness verification. Based on the 15-year simulation results, it was found that the middle low-permeability silt-dominated layers in the reservoir held the key to the gas recovery enhancement, and for the single-vertical-well system, the permeability improvement in this sublayer seemed more reliable and feasible than the aggressive depressurization. On the other hand, the dual-vertical-well system significantly exceeded the single-vertical-well system due to the synergistic effect of the two wellbores, and could raise the average gas production rate (9.5 × 103 m3/day) by one order of magnitude (to 7.9 × 104 m3/day). Moreover, if this new system was combined with the aggressive depressurization, the average gas production rate could be further raised by one order of magnitude (to 3.4 × 105 m3/day).

Abstract Kenya has made considerable policy efforts to expand its renewable energy portfolio to meet energy demand and mitigate greenhouse gas (GHG) emissions. Development of proper policies requires a robust framework for analyzing the benefits of renewable energy investments. Towards this end, this study applied a choice experiment analysis to determine how attributes (type of energy, ownership, impact on environment, distance and visibility, community job creation, and yearly renewable energy tax) impact the public willingness to pay for renewable energy development in Kenya. A nationwide survey of 1020 households was conducted in nine counties using conditional logit (MNL) and random parameter logit (RPL) frameworks. The results reveal that the Kenyan public places a high value on environmental impact, followed by type of renewable energy and community job creation, respectively. On the other hand, respondents do not place much emphasis on ownership or distance and visibility. Policy simulation suggests that while renewable energy adoption is highly valued by households, the total willingness to pay is not enough to cover the higher capital cost for the development of various renewable energy technologies.

Humidification dehumidification water desalination system using two cycles of air flow (open and closed-air cycles).

Integrated assessment models (IAMs) are playing an increasingly important role in long-run sustainability analysis. At their core is a set of global economic and environmental accounts which capture a complete set of inter-industry and inter-regional relationships in the global economy in a consistent manner. While much attention is focused on the raw data and parameterization required to expand or add sectoral detail to IAMs, only rarely is there discussion of how different matrix balancing methods (i.e. translating disparate raw data sources into the consistent database) affect modeling results. This article offers an in-depth look into the database–modeling nexus in IAMs, focusing on the electric power sector which is both a major source of CO2 emissions and a critical vehicle for climate change mitigation. Comparisons of the prevailing matrix balancing algorithms show how the choice of database reconciliation methodology affects modeling results using policy-relevant simulations in the context of the electric power sector. The resulting insights can be applied to the disaggregation of other, technology rich sectors in the economy. We conclude that appropriate selection of database reconciliation methodologies can reduce the deviation between bottom-up and top-down modeling.

The paper proposes a critical assessment of municipal solid waste gasification today, starting from basic aspects of the process (process types and steps, operating and performance parameters) and arriving to a comparative analysis of the reactors (fixed bed, fluidized bed, entrained bed, vertical shaft, moving grate furnace, rotary kiln, plasma reactor) as well as of the possible plant configurations (heat gasifier and power gasifier) and the environmental performances of the main commercially available gasifiers for municipal solid wastes. The analysis indicates that gasification is a technically viable option for the solid waste conversion, including residual waste from separate collection of municipal solid waste. It is able to meet existing emission limits and can have a remarkable effect on reduction of landfill disposal option.